Last updated: July 22, 2025

Reload lag data quality checks, SQL examples

A table-level check that calculates the maximum difference in days between ingestion timestamp and event timestamp values on any row. This check should be executed only as a partitioned check because this check finds the longest delay between the time that the row was created in the data source and the timestamp when the row was loaded into its daily or monthly partition. This check detects that a daily or monthly partition was reloaded, setting also the most recent timestamps in the created_at, loaded_at, inserted_at or other similar columns filled by the data pipeline or an ETL process during data loading.

---

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

daily partition reload lag

Check description

Daily partitioned check calculating the longest time a row waited to be loaded, it is the maximum difference in days between the ingestion timestamp and the event timestamp column on any row in the monitored partition

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
daily_partition_reload_lag	Reload lag (Maximum delay to load the last record for each partition)	timeliness	partitioned	daily	Timeliness	partition_reload_lag	max_days

Command-line examples

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

Managing daily partition reload lag 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_reload_lag --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_reload_lag --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_reload_lag --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_reload_lag --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_reload_lag --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_reload_lag --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_reload_lag check on all tables on a single data source.

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

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_reload_lag

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

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

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_reload_lag:
          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 partition_reload_lag data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_ingestion_event_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(
        TIMESTAMP_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATETIME_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP),
            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_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(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            analyzed_table."col_event_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,
    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_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(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ 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(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        BIGINT(SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP))
        -
        BIGINT(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_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
    MAX(
        BIGINT(SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP))
        -
        BIGINT(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_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(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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(DAYS_BETWEEN(
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE),
            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(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )) / 24.0 / 3600.0
    {%- else -%}
        MAX(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(SECONDS_BETWEEN(
            analyzed_table."col_inserted_at",
            analyzed_table."col_event_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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        NANO100_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DAYS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        NANO100_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    MAX(
        NANO100_BETWEEN(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(
        NANO100_BETWEEN(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000 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_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(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(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_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(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(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_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) - (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) - (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) - (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) - (CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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) - (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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP),
            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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    {%- 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            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_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
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            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_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(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ 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(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        BIGINT(SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP))
        -
        BIGINT(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_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
    MAX(
        BIGINT(SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP))
        -
        BIGINT(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_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(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        DATEDIFF(SECOND,
            analyzed_table.[col_event_timestamp],
            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_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(
          EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
          + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
          + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
          + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ 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' -%}
    MAX(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ 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_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
    MAX(
        EXTRACT(DAY FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP),
            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_reload_lag:
          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 partition_reload_lag sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_ingestion_event_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(
        TIMESTAMP_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATETIME_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP),
            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_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(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            analyzed_table."col_event_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,
    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_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(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ 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(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        BIGINT(SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP))
        -
        BIGINT(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_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
    MAX(
        BIGINT(SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP))
        -
        BIGINT(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_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(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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(DAYS_BETWEEN(
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE),
            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(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )) / 24.0 / 3600.0
    {%- else -%}
        MAX(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(SECONDS_BETWEEN(
            analyzed_table."col_inserted_at",
            analyzed_table."col_event_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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        NANO100_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DAYS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        NANO100_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    MAX(
        NANO100_BETWEEN(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(
        NANO100_BETWEEN(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000 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_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(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(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_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(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(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_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) - (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) - (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) - (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) - (CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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) - (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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP),
            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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    {%- 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            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_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
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            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_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(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ 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(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        BIGINT(SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP))
        -
        BIGINT(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_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
    MAX(
        BIGINT(SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP))
        -
        BIGINT(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_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(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        DATEDIFF(SECOND,
            analyzed_table.[col_event_timestamp],
            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_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(
          EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
          + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
          + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
          + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ 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' -%}
    MAX(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ 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_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
    MAX(
        EXTRACT(DAY FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP),
            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 reload lag

Check description

Monthly partitioned check calculating the longest time a row waited to be loaded, it is the maximum difference in days between the ingestion timestamp and the event timestamp column on any row in the monitored partition

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
monthly_partition_reload_lag	Reload lag (Maximum delay to load the last record for each partition)	timeliness	partitioned	monthly	Timeliness	partition_reload_lag	max_days

Command-line examples

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

Managing monthly partition reload lag 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_reload_lag --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_reload_lag --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_reload_lag --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_reload_lag --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_reload_lag --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_reload_lag --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_reload_lag check on all tables on a single data source.

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

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_reload_lag

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

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

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_reload_lag:
          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 partition_reload_lag data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_ingestion_event_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(
        TIMESTAMP_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATETIME_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP),
            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_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(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            analyzed_table."col_event_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,
    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_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(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ 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(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        BIGINT(SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP))
        -
        BIGINT(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_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
    MAX(
        BIGINT(SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP))
        -
        BIGINT(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_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(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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(DAYS_BETWEEN(
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE),
            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(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )) / 24.0 / 3600.0
    {%- else -%}
        MAX(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(SECONDS_BETWEEN(
            analyzed_table."col_inserted_at",
            analyzed_table."col_event_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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        NANO100_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DAYS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        NANO100_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    MAX(
        NANO100_BETWEEN(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(
        NANO100_BETWEEN(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000 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_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(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(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_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(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(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_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) - (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) - (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) - (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) - (CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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) - (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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP),
            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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    {%- 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            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_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
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            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_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(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ 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(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        BIGINT(SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP))
        -
        BIGINT(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_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
    MAX(
        BIGINT(SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP))
        -
        BIGINT(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_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(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        DATEDIFF(SECOND,
            analyzed_table.[col_event_timestamp],
            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_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(
          EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
          + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
          + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
          + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ 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' -%}
    MAX(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ 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_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
    MAX(
        EXTRACT(DAY FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP),
            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_reload_lag:
          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 partition_reload_lag sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_ingestion_event_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(
        TIMESTAMP_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATETIME_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP),
            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_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(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        DATE_DIFF(
            'MILLISECOND',
            analyzed_table."col_event_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,
    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_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(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ 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(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        BIGINT(SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP))
        -
        BIGINT(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_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
    MAX(
        BIGINT(SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP))
        -
        BIGINT(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_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(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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(DAYS_BETWEEN(
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS DATE),
            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(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )) / 24.0 / 3600.0
    {%- else -%}
        MAX(SECONDS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(SECONDS_BETWEEN(
            analyzed_table."col_inserted_at",
            analyzed_table."col_event_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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        NANO100_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DAYS_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        NANO100_BETWEEN(
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- else -%}
    MAX(
        NANO100_BETWEEN(
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(
        NANO100_BETWEEN(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 / 10000 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_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(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(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_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(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(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_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) - (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) - (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) - (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) - (CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS DATE)))
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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) - (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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP),
            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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    {%- 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') }} - {{ 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' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (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_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(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            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_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
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            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_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(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ 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(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        BIGINT({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        -
        BIGINT({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        BIGINT(SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP))
        -
        BIGINT(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_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
    MAX(
        BIGINT(SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP))
        -
        BIGINT(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_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(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_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
    MAX(
        DATEDIFF(SECOND,
            analyzed_table.[col_event_timestamp],
            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_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(
          EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
          + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
          + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
          + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ 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' -%}
    MAX(
        DATEDIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ 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' -%}
    MAX(
        EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(DAY FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP) - CAST({{ 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_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
    MAX(
        EXTRACT(DAY FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 86400
                  + EXTRACT(HOUR FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 3600
                  + EXTRACT(MINUTE FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP)) DAY(4) TO SECOND)) * 60
                  + EXTRACT(SECOND FROM ((CAST(analyzed_table."col_inserted_at" AS TIMESTAMP) - CAST(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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'DAY',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ 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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) AS DOUBLE) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    CAST(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            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_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(MAX(
        DATE_DIFF(
            'MILLISECOND',
            TRY_CAST(analyzed_table."col_event_timestamp" AS TIMESTAMP),
            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

---

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