Last updated: July 22, 2025

Data freshness anomaly data quality checks, SQL examples

This check calculates the most recent rows value and the current time and detects anomalies in a time series of previous averages. The timestamp column that is used for comparison is defined as the timestamp_columns.event_timestamp_column on the table configuration. It raises a data quality issue when the mean is in the top anomaly_percent percentage of the most outstanding values in the time series. This data quality check uses a 90-day time window and requires a history of at least 30 days.

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

profile data freshness anomaly

Check description

Verifies that the number of days since the most recent event timestamp (freshness) changes in a rate within a percentile boundary during the last 90 days.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
profile_data_freshness_anomaly	Data freshness anomaly (Abnormal delay in data delivery)	timeliness	profiling		Timeliness	data_freshness	anomaly_timeliness_delay

Command-line examples

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

Managing profile data freshness anomaly check from DQOps shell

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

YAML configuration

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  profiling_checks:
    timeliness:
      profile_data_freshness_anomaly:
        warning:
          anomaly_percent: 1.0
        error:
          anomaly_percent: 0.5
        fatal:
          anomaly_percent: 0.1
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

Samples of generated SQL queries for each data source type

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

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

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

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

{% macro render_current_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDate(now())
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDateTime(now())
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_current_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
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

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

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

DB2

Sensor template for DB2Rendered SQL for DB2

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

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

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

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

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

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

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

HANA

Sensor template for HANARendered SQL for HANA

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

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

SELECT
    {{ render_current_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
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        CURRENT_TIMESTAMP
    ) / 24.0 / 3600.0 / 1000.0 / 10000 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

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

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

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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

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

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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

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

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

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

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

Presto

Sensor template for PrestoRendered SQL for Presto

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

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

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

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

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

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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

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

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

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

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

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

Spark

Sensor template for SparkRendered SQL for Spark

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

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

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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

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

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

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

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

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

Trino

Sensor template for TrinoRendered SQL for Trino

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

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

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

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

Configuration with data grouping

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  profiling_checks:
    timeliness:
      profile_data_freshness_anomaly:
        warning:
          anomaly_percent: 1.0
        error:
          anomaly_percent: 0.5
        fatal:
          anomaly_percent: 0.1
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

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

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

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

{% macro render_current_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDate(now())
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDateTime(now())
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_current_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
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

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

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

DB2

Sensor template for DB2Rendered SQL for DB2

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

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

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

                analyzed_table.grouping_level_1,

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

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

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

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

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

HANA

Sensor template for HANARendered SQL for HANA

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

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

SELECT
    {{ render_current_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
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        CURRENT_TIMESTAMP
    ) / 24.0 / 3600.0 / 1000.0 / 10000 AS actual_value,

                analyzed_table.grouping_level_1,

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

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

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

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

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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

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

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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

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

SELECT
    {{ render_current_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(CURRENT_TIMESTAMP AS DATE) - CAST(MAX(analyzed_table."col_event_timestamp") AS DATE)) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

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

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

Presto

Sensor template for PrestoRendered SQL for Presto

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

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

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

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

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

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

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

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

                analyzed_table.grouping_level_1,

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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

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

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

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

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

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

Spark

Sensor template for SparkRendered SQL for Spark

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

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

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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

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

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

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

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

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

Trino

Sensor template for TrinoRendered SQL for Trino

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

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

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

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

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

---

daily data freshness anomaly

Check description

Verifies that the number of days since the most recent event timestamp (freshness) changes in a rate within a percentile boundary during the last 90 days.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
daily_data_freshness_anomaly	Data freshness anomaly (Abnormal delay in data delivery)	timeliness	monitoring	daily	Timeliness	data_freshness	anomaly_timeliness_delay

Command-line examples

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

Managing daily data freshness anomaly check from DQOps shell

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

YAML configuration

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  monitoring_checks:
    daily:
      timeliness:
        daily_data_freshness_anomaly:
          warning:
            anomaly_percent: 1.0
          error:
            anomaly_percent: 0.5
          fatal:
            anomaly_percent: 0.1
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

Samples of generated SQL queries for each data source type

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

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

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

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

{% macro render_current_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDate(now())
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDateTime(now())
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_current_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
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

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

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

DB2

Sensor template for DB2Rendered SQL for DB2

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

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

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

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

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

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

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

HANA

Sensor template for HANARendered SQL for HANA

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

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

SELECT
    {{ render_current_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
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        CURRENT_TIMESTAMP
    ) / 24.0 / 3600.0 / 1000.0 / 10000 AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

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

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

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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

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

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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

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

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

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

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

Presto

Sensor template for PrestoRendered SQL for Presto

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

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

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

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

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

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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

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

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

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

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

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

Spark

Sensor template for SparkRendered SQL for Spark

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

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

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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

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

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

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

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

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

Trino

Sensor template for TrinoRendered SQL for Trino

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

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

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

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

Configuration with data grouping

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  monitoring_checks:
    daily:
      timeliness:
        daily_data_freshness_anomaly:
          warning:
            anomaly_percent: 1.0
          error:
            anomaly_percent: 0.5
          fatal:
            anomaly_percent: 0.1
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

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

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

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

{% macro render_current_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'DAY',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDate(now())
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
        toDateTime(now())
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}, 3)
        ),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_current_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
    DATE_DIFF(
        'MILLISECOND',
        MAX(
            toDateTime64OrNull(analyzed_table."col_event_timestamp", 3)
        ),
        toDateTime64(now(), 3)
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

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

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

DB2

Sensor template for DB2Rendered SQL for DB2

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

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

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

                analyzed_table.grouping_level_1,

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

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

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

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

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

HANA

Sensor template for HANARendered SQL for HANA

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

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

SELECT
    {{ render_current_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
    NANO100_BETWEEN(
        MAX(
            TO_TIMESTAMP(analyzed_table."col_event_timestamp")
        ),
        CURRENT_TIMESTAMP
    ) / 24.0 / 3600.0 / 1000.0 / 10000 AS actual_value,

                analyzed_table.grouping_level_1,

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

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

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

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

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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

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

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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

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

SELECT
    {{ render_current_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(CURRENT_TIMESTAMP AS DATE) - CAST(MAX(analyzed_table."col_event_timestamp") AS DATE)) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

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

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

Presto

Sensor template for PrestoRendered SQL for Presto

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

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

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

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

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

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

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

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

                analyzed_table.grouping_level_1,

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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

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

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

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

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

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

Spark

Sensor template for SparkRendered SQL for Spark

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

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

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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

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

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

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

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

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

Trino

Sensor template for TrinoRendered SQL for Trino

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

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

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

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

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

---

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