Last updated: July 22, 2025

Data ingestion delay data quality checks, SQL examples

A table-level check that calculates the time difference between the most recent row in the table and the most recent timestamp when the last row was loaded into the data warehouse or data lake. To identify the most recent row, the check finds the maximum value of the timestamp column that should contain the last modification timestamp from the source. The timestamp when the row was loaded is identified by the most recent (maximum) value a timestamp column that was filled by the data pipeline, for example: "loaded_at", "updated_at", etc. This check requires that the data pipeline is filling an extra column with the timestamp when the data loading job has been executed. The names of both columns used for comparison should be specified in the "timestamp_columns" configuration entry on the table.

---

The data ingestion delay data quality check has the following variants for each type of data quality checks supported by DQOps.

profile data ingestion delay

Check description

Calculates the time difference in days between the most recent event timestamp and the most recent ingestion timestamp

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
profile_data_ingestion_delay	Data ingestion delay (Maximum number of days between the last record has been created and loaded)	timeliness	profiling		Timeliness	data_ingestion_delay	max_days

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the profile data ingestion delay data quality check.

Managing profile data ingestion delay check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=profile_data_ingestion_delay --enable-warning

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=profile_data_ingestion_delay --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=profile_data_ingestion_delay --enable-warning
                    -Wmax_days=value

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=profile_data_ingestion_delay --enable-error

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=profile_data_ingestion_delay --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=profile_data_ingestion_delay --enable-error
                    -Emax_days=value

Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the profile_data_ingestion_delay check on all tables on a single data source.

dqo> check run -c=data_source_name -ch=profile_data_ingestion_delay

It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.

dqo> check run -c=connection_name -t=schema_name.table_name -ch=profile_data_ingestion_delay

You can also run this check on all tables on which the profile_data_ingestion_delay check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_*  -ch=profile_data_ingestion_delay

YAML configuration

The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  profiling_checks:
    timeliness:
      profile_data_ingestion_delay:
        warning:
          max_days: 1.0
        error:
          max_days: 2.0
        fatal:
          max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

Samples of generated SQL queries for each data source type

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the data_ingestion_delay data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMP_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        DAY
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATETIME_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY'
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        MAX(
            toDateTime64OrNull(analyzed_table."col_inserted_at"), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DAYS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
        ) / 24.0 / 3600.0
    {%- else -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    SECONDS_BETWEEN(
            MAX(CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)),
            MAX((CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)))
        ) / 24.0 / 3600.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM  AS analyzed_table

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DAYS_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
        ),
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        MAX(
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_table>` AS analyzed_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_table>` AS analyzed_table

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
         MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))

    {%- else -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(CAST(analyzed_table."col_inserted_at" AS DATE)) - MAX((CAST(analyzed_table."col_event_timestamp" AS DATE))) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_table>" original_table
) analyzed_table

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true' and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})),
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp")),
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at"))
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DATEDIFF(SECOND,
            MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATEDIFF(SECOND,
        MAX(analyzed_table.[col_event_timestamp]),
        MAX(analyzed_table.[col_inserted_at])
    ) / 24.0 / 3600.0 AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        EXTRACT(DAY FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0 AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table

Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).

Configuration with data grouping

Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  profiling_checks:
    timeliness:
      profile_data_ingestion_delay:
        warning:
          max_days: 1.0
        error:
          max_days: 2.0
        fatal:
          max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the data_ingestion_delay sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMP_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        DAY
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATETIME_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY'
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        MAX(
            toDateTime64OrNull(analyzed_table."col_inserted_at"), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DAYS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
        ) / 24.0 / 3600.0
    {%- else -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    SECONDS_BETWEEN(
            MAX(CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)),
            MAX((CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)))
        ) / 24.0 / 3600.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM  AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DAYS_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
        ),
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        MAX(
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
         MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))

    {%- else -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(CAST(analyzed_table."col_inserted_at" AS DATE)) - MAX((CAST(analyzed_table."col_event_timestamp" AS DATE))) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true' and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})),
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp")),
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at"))
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DATEDIFF(SECOND,
            MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATEDIFF(SECOND,
        MAX(analyzed_table.[col_event_timestamp]),
        MAX(analyzed_table.[col_inserted_at])
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        EXTRACT(DAY FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

---

daily data ingestion delay

Check description

Daily calculating the time difference in days between the most recent event timestamp and the most recent ingestion timestamp

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
daily_data_ingestion_delay	Data ingestion delay (Maximum number of days between the last record has been created and loaded)	timeliness	monitoring	daily	Timeliness	data_ingestion_delay	max_days

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the daily data ingestion delay data quality check.

Managing daily data ingestion delay check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=daily_data_ingestion_delay --enable-warning

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_data_ingestion_delay --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_data_ingestion_delay --enable-warning
                    -Wmax_days=value

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=daily_data_ingestion_delay --enable-error

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_data_ingestion_delay --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_data_ingestion_delay --enable-error
                    -Emax_days=value

Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the daily_data_ingestion_delay check on all tables on a single data source.

dqo> check run -c=data_source_name -ch=daily_data_ingestion_delay

It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.

dqo> check run -c=connection_name -t=schema_name.table_name -ch=daily_data_ingestion_delay

You can also run this check on all tables on which the daily_data_ingestion_delay check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_data_ingestion_delay

YAML configuration

The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  monitoring_checks:
    daily:
      timeliness:
        daily_data_ingestion_delay:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

Samples of generated SQL queries for each data source type

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the data_ingestion_delay data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMP_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        DAY
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATETIME_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY'
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        MAX(
            toDateTime64OrNull(analyzed_table."col_inserted_at"), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DAYS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
        ) / 24.0 / 3600.0
    {%- else -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    SECONDS_BETWEEN(
            MAX(CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)),
            MAX((CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)))
        ) / 24.0 / 3600.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM  AS analyzed_table

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DAYS_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
        ),
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        MAX(
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_table>` AS analyzed_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_table>` AS analyzed_table

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
         MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))

    {%- else -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(CAST(analyzed_table."col_inserted_at" AS DATE)) - MAX((CAST(analyzed_table."col_event_timestamp" AS DATE))) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_table>" original_table
) analyzed_table

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true' and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})),
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp")),
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at"))
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DATEDIFF(SECOND,
            MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATEDIFF(SECOND,
        MAX(analyzed_table.[col_event_timestamp]),
        MAX(analyzed_table.[col_inserted_at])
    ) / 24.0 / 3600.0 AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        EXTRACT(DAY FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0 AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table

Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).

Configuration with data grouping

Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  monitoring_checks:
    daily:
      timeliness:
        daily_data_ingestion_delay:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the data_ingestion_delay sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMP_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        DAY
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATETIME_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY'
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        MAX(
            toDateTime64OrNull(analyzed_table."col_inserted_at"), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DAYS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
        ) / 24.0 / 3600.0
    {%- else -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    SECONDS_BETWEEN(
            MAX(CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)),
            MAX((CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)))
        ) / 24.0 / 3600.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM  AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DAYS_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
        ),
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        MAX(
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
         MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))

    {%- else -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(CAST(analyzed_table."col_inserted_at" AS DATE)) - MAX((CAST(analyzed_table."col_event_timestamp" AS DATE))) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true' and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})),
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp")),
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at"))
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DATEDIFF(SECOND,
            MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATEDIFF(SECOND,
        MAX(analyzed_table.[col_event_timestamp]),
        MAX(analyzed_table.[col_inserted_at])
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        EXTRACT(DAY FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

---

monthly data ingestion delay

Check description

Monthly monitoring calculating the time difference in days between the most recent event timestamp and the most recent ingestion timestamp

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
monthly_data_ingestion_delay	Data ingestion delay (Maximum number of days between the last record has been created and loaded)	timeliness	monitoring	monthly	Timeliness	data_ingestion_delay	max_days

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the monthly data ingestion delay data quality check.

Managing monthly data ingestion delay check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=monthly_data_ingestion_delay --enable-warning

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_data_ingestion_delay --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_data_ingestion_delay --enable-warning
                    -Wmax_days=value

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=monthly_data_ingestion_delay --enable-error

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_data_ingestion_delay --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_data_ingestion_delay --enable-error
                    -Emax_days=value

Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the monthly_data_ingestion_delay check on all tables on a single data source.

dqo> check run -c=data_source_name -ch=monthly_data_ingestion_delay

It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.

dqo> check run -c=connection_name -t=schema_name.table_name -ch=monthly_data_ingestion_delay

You can also run this check on all tables on which the monthly_data_ingestion_delay check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_data_ingestion_delay

YAML configuration

The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  monitoring_checks:
    monthly:
      timeliness:
        monthly_data_ingestion_delay:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

Samples of generated SQL queries for each data source type

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the data_ingestion_delay data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMP_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        DAY
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATETIME_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY'
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        MAX(
            toDateTime64OrNull(analyzed_table."col_inserted_at"), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DAYS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
        ) / 24.0 / 3600.0
    {%- else -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    SECONDS_BETWEEN(
            MAX(CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)),
            MAX((CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)))
        ) / 24.0 / 3600.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM  AS analyzed_table

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DAYS_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
        ),
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        MAX(
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_table>` AS analyzed_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_table>` AS analyzed_table

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
         MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))

    {%- else -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(CAST(analyzed_table."col_inserted_at" AS DATE)) - MAX((CAST(analyzed_table."col_event_timestamp" AS DATE))) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_table>" original_table
) analyzed_table

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true' and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})),
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp")),
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at"))
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DATEDIFF(SECOND,
            MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATEDIFF(SECOND,
        MAX(analyzed_table.[col_event_timestamp]),
        MAX(analyzed_table.[col_inserted_at])
    ) / 24.0 / 3600.0 AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        EXTRACT(DAY FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0 AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table

Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).

Configuration with data grouping

Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  monitoring_checks:
    monthly:
      timeliness:
        monthly_data_ingestion_delay:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the data_ingestion_delay sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMP_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        DAY
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATETIME_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY'
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        MAX(
            toDateTime64OrNull(analyzed_table."col_inserted_at"), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DAYS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
        ) / 24.0 / 3600.0
    {%- else -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    SECONDS_BETWEEN(
            MAX(CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)),
            MAX((CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)))
        ) / 24.0 / 3600.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM  AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DAYS_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
        ),
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        MAX(
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
         MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))

    {%- else -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(CAST(analyzed_table."col_inserted_at" AS DATE)) - MAX((CAST(analyzed_table."col_event_timestamp" AS DATE))) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true' and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})),
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp")),
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at"))
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DATEDIFF(SECOND,
            MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATEDIFF(SECOND,
        MAX(analyzed_table.[col_event_timestamp]),
        MAX(analyzed_table.[col_inserted_at])
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        EXTRACT(DAY FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

---

daily partition data ingestion delay

Check description

Daily partitioned check calculating the time difference in days between the most recent event timestamp and the most recent ingestion timestamp

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
daily_partition_data_ingestion_delay	Data ingestion delay (Maximum number of days between the last record has been created and loaded)	timeliness	partitioned	daily	Timeliness	data_ingestion_delay	max_days

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the daily partition data ingestion delay data quality check.

Managing daily partition data ingestion delay check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=daily_partition_data_ingestion_delay --enable-warning

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_partition_data_ingestion_delay --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_partition_data_ingestion_delay --enable-warning
                    -Wmax_days=value

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=daily_partition_data_ingestion_delay --enable-error

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_partition_data_ingestion_delay --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_partition_data_ingestion_delay --enable-error
                    -Emax_days=value

Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the daily_partition_data_ingestion_delay check on all tables on a single data source.

dqo> check run -c=data_source_name -ch=daily_partition_data_ingestion_delay

It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.

dqo> check run -c=connection_name -t=schema_name.table_name -ch=daily_partition_data_ingestion_delay

You can also run this check on all tables on which the daily_partition_data_ingestion_delay check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_partition_data_ingestion_delay

YAML configuration

The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  partitioned_checks:
    daily:
      timeliness:
        daily_partition_data_ingestion_delay:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"

Samples of generated SQL queries for each data source type

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the data_ingestion_delay data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMP_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        DAY
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATETIME_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY'
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        MAX(
            toDateTime64OrNull(analyzed_table."col_inserted_at"), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    CAST(analyzed_table."date_column" AS DATE) AS time_period,
    toDateTime64(CAST(analyzed_table."date_column" AS DATE), 3) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DAYS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
        ) / 24.0 / 3600.0
    {%- else -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    SECONDS_BETWEEN(
            MAX(CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)),
            MAX((CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)))
        ) / 24.0 / 3600.0 AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM  AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DAYS_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
        ),
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        MAX(
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TO_TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
         MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))

    {%- else -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(CAST(analyzed_table."col_inserted_at" AS DATE)) - MAX((CAST(analyzed_table."col_event_timestamp" AS DATE))) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(original_table."date_column" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS date) AS time_period,
    CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    CAST(DATE_TRUNC('day', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('day', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true' and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})),
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp")),
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at"))
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    TO_TIMESTAMP(CAST(analyzed_table."date_column" AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DATEDIFF(SECOND,
            MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATEDIFF(SECOND,
        MAX(analyzed_table.[col_event_timestamp]),
        MAX(analyzed_table.[col_inserted_at])
    ) / 24.0 / 3600.0 AS actual_value,
    CAST(analyzed_table.[date_column] AS date) AS time_period,
    CAST((CAST(analyzed_table.[date_column] AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY CAST(analyzed_table.[date_column] AS date), CAST(analyzed_table.[date_column] AS date)
ORDER BY CAST(analyzed_table.[date_column] AS date)

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        EXTRACT(DAY FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0 AS actual_value,
    CAST(analyzed_table."date_column" AS DATE) AS time_period,
    CAST(CAST(analyzed_table."date_column" AS DATE) AS TIMESTAMP) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS date) AS time_period,
    CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).

Configuration with data grouping

Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  partitioned_checks:
    daily:
      timeliness:
        daily_partition_data_ingestion_delay:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the data_ingestion_delay sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMP_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        DAY
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATETIME_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY'
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        MAX(
            toDateTime64OrNull(analyzed_table."col_inserted_at"), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS DATE) AS time_period,
    toDateTime64(CAST(analyzed_table."date_column" AS DATE), 3) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DAYS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
        ) / 24.0 / 3600.0
    {%- else -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    SECONDS_BETWEEN(
            MAX(CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)),
            MAX((CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)))
        ) / 24.0 / 3600.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM  AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DAYS_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
        ),
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        MAX(
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TO_TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
         MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))

    {%- else -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(CAST(analyzed_table."col_inserted_at" AS DATE)) - MAX((CAST(analyzed_table."col_event_timestamp" AS DATE))) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    TRUNC(CAST(original_table."date_column" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(original_table."date_column" AS date) AS time_period,
    CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(DATE_TRUNC('day', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('day', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true' and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})),
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp")),
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at"))
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    TO_TIMESTAMP(CAST(analyzed_table."date_column" AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DATEDIFF(SECOND,
            MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATEDIFF(SECOND,
        MAX(analyzed_table.[col_event_timestamp]),
        MAX(analyzed_table.[col_inserted_at])
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    CAST(analyzed_table.[date_column] AS date) AS time_period,
    CAST((CAST(analyzed_table.[date_column] AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state], CAST(analyzed_table.[date_column] AS date), CAST(analyzed_table.[date_column] AS date)
ORDER BY level_1, level_2CAST(analyzed_table.[date_column] AS date)

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        EXTRACT(DAY FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS DATE) AS time_period,
    CAST(CAST(analyzed_table."date_column" AS DATE) AS TIMESTAMP) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(original_table."date_column" AS date) AS time_period,
    CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

---

monthly partition data ingestion delay

Check description

Monthly partitioned check calculating the time difference in days between the most recent event timestamp and the most recent ingestion timestamp

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
monthly_partition_data_ingestion_delay	Data ingestion delay (Maximum number of days between the last record has been created and loaded)	timeliness	partitioned	monthly	Timeliness	data_ingestion_delay	max_days

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the monthly partition data ingestion delay data quality check.

Managing monthly partition data ingestion delay check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=monthly_partition_data_ingestion_delay --enable-warning

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_partition_data_ingestion_delay --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_partition_data_ingestion_delay --enable-warning
                    -Wmax_days=value

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=monthly_partition_data_ingestion_delay --enable-error

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_partition_data_ingestion_delay --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_partition_data_ingestion_delay --enable-error
                    -Emax_days=value

Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the monthly_partition_data_ingestion_delay check on all tables on a single data source.

dqo> check run -c=data_source_name -ch=monthly_partition_data_ingestion_delay

It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.

dqo> check run -c=connection_name -t=schema_name.table_name -ch=monthly_partition_data_ingestion_delay

You can also run this check on all tables on which the monthly_partition_data_ingestion_delay check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_partition_data_ingestion_delay

YAML configuration

The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  partitioned_checks:
    monthly:
      timeliness:
        monthly_partition_data_ingestion_delay:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"

Samples of generated SQL queries for each data source type

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the data_ingestion_delay data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMP_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        DAY
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATETIME_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY'
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        MAX(
            toDateTime64OrNull(analyzed_table."col_inserted_at"), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)) AS time_period,
    toDateTime64(DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)), 3) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DAYS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
        ) / 24.0 / 3600.0
    {%- else -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    SECONDS_BETWEEN(
            MAX(CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)),
            MAX((CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)))
        ) / 24.0 / 3600.0 AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE))) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM  AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DAYS_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
        ),
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        MAX(
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN) AS time_period,
    TO_TIMESTAMP(SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
         MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))

    {%- else -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(CAST(analyzed_table."col_inserted_at" AS DATE)) - MAX((CAST(analyzed_table."col_event_timestamp" AS DATE))) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
    CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    CAST(DATE_TRUNC('month', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('month', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true' and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})),
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp")),
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at"))
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DATEDIFF(SECOND,
            MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATEDIFF(SECOND,
        MAX(analyzed_table.[col_event_timestamp]),
        MAX(analyzed_table.[col_inserted_at])
    ) / 24.0 / 3600.0 AS actual_value,
    DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1) AS time_period,
    CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[date_column]), 0)
ORDER BY DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        EXTRACT(DAY FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0 AS actual_value,
    TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS time_period,
    CAST(TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS TIMESTAMP) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
    CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).

Configuration with data grouping

Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  partitioned_checks:
    monthly:
      timeliness:
        monthly_partition_data_ingestion_delay:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the data_ingestion_delay sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMP_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        DAY
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATETIME_DIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMP_DIFF(
        MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ),
        MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ),
        MILLISECOND
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY'
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        MAX(
            toDateTime64OrNull(analyzed_table."col_inserted_at"), 3)
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)) AS time_period,
    toDateTime64(DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)), 3) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DAYS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
        ) / 24.0 / 3600.0
    {%- else -%}
        SECONDS_BETWEEN(
            MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)),
            MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)))
        ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    SECONDS_BETWEEN(
            MAX(CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)),
            MAX((CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)))
        ) / 24.0 / 3600.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE))) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM  AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DAYS_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    NANO100_BETWEEN(
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
        ),
        MAX(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        MAX(
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN) AS time_period,
    TO_TIMESTAMP(SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    TIMESTAMPDIFF(
        SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        SECOND,
        MAX(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
        MAX(analyzed_table.`col_inserted_at` AS TIMESTAMP)
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
         MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))

    {%- else -%}
     MAX(CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE)) - MAX((CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(CAST(analyzed_table."col_inserted_at" AS DATE)) - MAX((CAST(analyzed_table."col_event_timestamp" AS DATE))) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
    CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(DATE_TRUNC('month', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('month', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    EXTRACT(EPOCH FROM (
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) - MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )) / 24.0 / 3600.0
    {%- else -%}
    EXTRACT(EPOCH FROM (
        MAX(({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP) - MAX(({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP)
    )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    EXTRACT(EPOCH FROM (
        MAX((analyzed_table."col_inserted_at")::TIMESTAMP) - MAX((analyzed_table."col_event_timestamp")::TIMESTAMP)
    )) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true' and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})),
        MAX(TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    TIMESTAMPDIFF(
        MILLISECOND,
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp")),
        MAX(TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at"))
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}))
        -
        BIGINT(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        ))
        -
        BIGINT(MAX(
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP)
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
        DATEDIFF(SECOND,
            MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(DAY,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    DATEDIFF(SECOND,
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    DATEDIFF(SECOND,
        MAX(analyzed_table.[col_event_timestamp]),
        MAX(analyzed_table.[col_inserted_at])
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1) AS time_period,
    CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state], DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[date_column]), 0)
ORDER BY level_1, level_2DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATEDIFF(
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    (
        EXTRACT(DAY FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)
            - CAST(MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}) AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (
        EXTRACT(DAY FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
        + EXTRACT(HOUR FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
        + EXTRACT(MINUTE FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
        + EXTRACT(SECOND FROM ((CAST(MAX(analyzed_table."col_inserted_at") AS TIMESTAMP)
            - CAST(MAX(analyzed_table."col_event_timestamp") AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS time_period,
    CAST(TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS TIMESTAMP) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro render_ingestion_event_max_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE)
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        MAX({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_max_diff() }} AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CAST(DATE_DIFF(
        'MILLISECOND',
        MAX(
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)
        ),
        MAX(
            TRY_CAST(analyzed_table."col_inserted_at" AS TIMESTAMP)
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0 AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
    CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

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What's next

• Learn how to configure data quality checks in DQOps
• Look at the examples of running data quality checks, targeting tables and columns