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Last updated: July 22, 2025

Sql aggregate expression on column data quality checks, SQL examples

A column-level check that calculates a given SQL aggregate expression on a column and verifies if the value is within a range of accepted values.

The sql aggregate expression on column data quality check has the following variants for each type of data quality checks supported by DQOps.

profile sql aggregate expression on column

Check description

Verifies that a custom aggregated SQL expression (MIN, MAX, etc.) is not outside the expected range.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
profile_sql_aggregate_expression_on_column Custom aggregated SQL expression within range custom_sql profiling Reasonableness sql_aggregated_expression between_floats

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the profile sql aggregate expression on column data quality check.

Managing profile sql aggregate expression on column check from DQOps shell

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 -col=column_name -ch=profile_sql_aggregate_expression_on_column --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_* -col=column_name -ch=profile_sql_aggregate_expression_on_column --enable-warning

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=profile_sql_aggregate_expression_on_column --enable-warning
                    -Wfrom=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 -col=column_name -ch=profile_sql_aggregate_expression_on_column --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_* -col=column_name -ch=profile_sql_aggregate_expression_on_column --enable-error

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=profile_sql_aggregate_expression_on_column --enable-error
                    -Efrom=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_sql_aggregate_expression_on_column check on all tables and columns on a single data source.

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

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_sql_aggregate_expression_on_column

You can also run this check on all tables (and columns) on which the profile_sql_aggregate_expression_on_column check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=profile_sql_aggregate_expression_on_column

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:
  columns:
    target_column:
      profiling_checks:
        custom_sql:
          profile_sql_aggregate_expression_on_column:
            parameters:
              sql_expression: "MAX({column})"
            error:
              from: 10.0
              to: 20.5
      labels:
      - This is the column that is analyzed for data quality issues
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 sql_aggregated_expression data quality sensor.

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
ClickHouse 
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Databricks 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
DB2 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
DuckDB 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM  AS analyzed_table
HANA 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
MariaDB 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_table>` AS analyzed_table
MySQL 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_table>` AS analyzed_table
Oracle 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
PostgreSQL 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
Presto 
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
QuestDB 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_table>" original_table
) analyzed_table
Redshift 
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
Snowflake 
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
Spark 
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
SQL Server 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.[target_column])) AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
Teradata 
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Trino 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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:
  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
  columns:
    target_column:
      profiling_checks:
        custom_sql:
          profile_sql_aggregate_expression_on_column:
            parameters:
              sql_expression: "MAX({column})"
            error:
              from: 10.0
              to: 20.5
      labels:
      - This is the column that is analyzed for data quality issues
    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 sql_aggregated_expression sensor.

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.[target_column])) 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 
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 sql aggregate expression on column

Check description

Verifies that a custom aggregated SQL expression (MIN, MAX, etc.) is not outside the expected range. Stores the most recent captured value for each day when the data quality check was evaluated.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
daily_sql_aggregate_expression_on_column Custom aggregated SQL expression within range custom_sql monitoring daily Reasonableness sql_aggregated_expression between_floats

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the daily sql aggregate expression on column data quality check.

Managing daily sql aggregate expression on column check from DQOps shell

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 -col=column_name -ch=daily_sql_aggregate_expression_on_column --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_* -col=column_name -ch=daily_sql_aggregate_expression_on_column --enable-warning

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_sql_aggregate_expression_on_column --enable-warning
                    -Wfrom=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 -col=column_name -ch=daily_sql_aggregate_expression_on_column --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_* -col=column_name -ch=daily_sql_aggregate_expression_on_column --enable-error

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_sql_aggregate_expression_on_column --enable-error
                    -Efrom=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_sql_aggregate_expression_on_column check on all tables and columns on a single data source.

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

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_sql_aggregate_expression_on_column

You can also run this check on all tables (and columns) on which the daily_sql_aggregate_expression_on_column check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=daily_sql_aggregate_expression_on_column

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:
  columns:
    target_column:
      monitoring_checks:
        daily:
          custom_sql:
            daily_sql_aggregate_expression_on_column:
              parameters:
                sql_expression: "MAX({column})"
              error:
                from: 10.0
                to: 20.5
      labels:
      - This is the column that is analyzed for data quality issues
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 sql_aggregated_expression data quality sensor.

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
ClickHouse 
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Databricks 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
DB2 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
DuckDB 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM  AS analyzed_table
HANA 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
MariaDB 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_table>` AS analyzed_table
MySQL 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_table>` AS analyzed_table
Oracle 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
PostgreSQL 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
Presto 
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
QuestDB 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_table>" original_table
) analyzed_table
Redshift 
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
Snowflake 
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
Spark 
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
SQL Server 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.[target_column])) AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
Teradata 
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Trino 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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:
  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
  columns:
    target_column:
      monitoring_checks:
        daily:
          custom_sql:
            daily_sql_aggregate_expression_on_column:
              parameters:
                sql_expression: "MAX({column})"
              error:
                from: 10.0
                to: 20.5
      labels:
      - This is the column that is analyzed for data quality issues
    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 sql_aggregated_expression sensor.

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.[target_column])) 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 
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

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

monthly sql aggregate expression on column

Check description

Verifies that a custom aggregated SQL expression (MIN, MAX, etc.) is not outside the expected range. Stores the most recent check result for each month when the data quality check was evaluated.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
monthly_sql_aggregate_expression_on_column Custom aggregated SQL expression within range custom_sql monitoring monthly Reasonableness sql_aggregated_expression between_floats

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the monthly sql aggregate expression on column data quality check.

Managing monthly sql aggregate expression on column check from DQOps shell

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 -col=column_name -ch=monthly_sql_aggregate_expression_on_column --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_* -col=column_name -ch=monthly_sql_aggregate_expression_on_column --enable-warning

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=monthly_sql_aggregate_expression_on_column --enable-warning
                    -Wfrom=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 -col=column_name -ch=monthly_sql_aggregate_expression_on_column --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_* -col=column_name -ch=monthly_sql_aggregate_expression_on_column --enable-error

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=monthly_sql_aggregate_expression_on_column --enable-error
                    -Efrom=value

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

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

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

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

You can also run this check on all tables (and columns) on which the monthly_sql_aggregate_expression_on_column check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=monthly_sql_aggregate_expression_on_column

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:
  columns:
    target_column:
      monitoring_checks:
        monthly:
          custom_sql:
            monthly_sql_aggregate_expression_on_column:
              parameters:
                sql_expression: "MAX({column})"
              error:
                from: 10.0
                to: 20.5
      labels:
      - This is the column that is analyzed for data quality issues
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 sql_aggregated_expression data quality sensor.

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
ClickHouse 
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Databricks 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
DB2 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
DuckDB 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM  AS analyzed_table
HANA 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
MariaDB 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_table>` AS analyzed_table
MySQL 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_table>` AS analyzed_table
Oracle 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
PostgreSQL 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
Presto 
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
QuestDB 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_table>" original_table
) analyzed_table
Redshift 
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
Snowflake 
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
Spark 
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
SQL Server 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.[target_column])) AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
Teradata 
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Trino 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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:
  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
  columns:
    target_column:
      monitoring_checks:
        monthly:
          custom_sql:
            monthly_sql_aggregate_expression_on_column:
              parameters:
                sql_expression: "MAX({column})"
              error:
                from: 10.0
                to: 20.5
      labels:
      - This is the column that is analyzed for data quality issues
    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 sql_aggregated_expression sensor.

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) 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 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.[target_column])) 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 
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) 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 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

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

daily partition sql aggregate expression on column

Check description

Verifies that a custom aggregated SQL expression (MIN, MAX, etc.) is not outside the expected range. Stores a separate data quality check result for each daily partition.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
daily_partition_sql_aggregate_expression_on_column Custom aggregated SQL expression within range custom_sql partitioned daily Reasonableness sql_aggregated_expression between_floats

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the daily partition sql aggregate expression on column data quality check.

Managing daily partition sql aggregate expression on column check from DQOps shell

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 -col=column_name -ch=daily_partition_sql_aggregate_expression_on_column --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_* -col=column_name -ch=daily_partition_sql_aggregate_expression_on_column --enable-warning

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_partition_sql_aggregate_expression_on_column --enable-warning
                    -Wfrom=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 -col=column_name -ch=daily_partition_sql_aggregate_expression_on_column --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_* -col=column_name -ch=daily_partition_sql_aggregate_expression_on_column --enable-error

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_partition_sql_aggregate_expression_on_column --enable-error
                    -Efrom=value

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

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

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

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

You can also run this check on all tables (and columns) on which the daily_partition_sql_aggregate_expression_on_column check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=daily_partition_sql_aggregate_expression_on_column

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:
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  columns:
    target_column:
      partitioned_checks:
        daily:
          custom_sql:
            daily_partition_sql_aggregate_expression_on_column:
              parameters:
                sql_expression: "MAX({column})"
              error:
                from: 10.0
                to: 20.5
      labels:
      - This is the column that is analyzed for data quality issues
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"
Samples of generated SQL queries for each data source type

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

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
ClickHouse 
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    CAST(analyzed_table."date_column" AS DATE) AS time_period,
    toDateTime64(CAST(analyzed_table."date_column" AS DATE), 3) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Databricks 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
DB2 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
DuckDB 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM  AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
HANA 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TO_TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
MariaDB 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
MySQL 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Oracle 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(original_table."date_column" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
PostgreSQL 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Presto 
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS date) AS time_period,
    CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
QuestDB 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    CAST(DATE_TRUNC('day', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('day', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Redshift 
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Snowflake 
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    TO_TIMESTAMP(CAST(analyzed_table."date_column" AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Spark 
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
SQL Server 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.[target_column])) AS actual_value,
    CAST(analyzed_table.[date_column] AS date) AS time_period,
    CAST((CAST(analyzed_table.[date_column] AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY CAST(analyzed_table.[date_column] AS date), CAST(analyzed_table.[date_column] AS date)
ORDER BY CAST(analyzed_table.[date_column] AS date)
Teradata 
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    CAST(analyzed_table."date_column" AS DATE) AS time_period,
    CAST(CAST(analyzed_table."date_column" AS DATE) AS TIMESTAMP) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Trino 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS date) AS time_period,
    CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

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

Configuration with data grouping

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  columns:
    target_column:
      partitioned_checks:
        daily:
          custom_sql:
            daily_partition_sql_aggregate_expression_on_column:
              parameters:
                sql_expression: "MAX({column})"
              error:
                from: 10.0
                to: 20.5
      labels:
      - This is the column that is analyzed for data quality issues
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

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

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ClickHouse 
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS DATE) AS time_period,
    toDateTime64(CAST(analyzed_table."date_column" AS DATE), 3) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Databricks 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
DB2 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
DuckDB 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM  AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
HANA 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TO_TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
MariaDB 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
MySQL 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Oracle 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    TRUNC(CAST(original_table."date_column" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
PostgreSQL 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Presto 
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(original_table."date_column" AS date) AS time_period,
    CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
QuestDB 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(DATE_TRUNC('day', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('day', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Redshift 
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Snowflake 
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    TO_TIMESTAMP(CAST(analyzed_table."date_column" AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Spark 
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
SQL Server 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.[target_column])) AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    CAST(analyzed_table.[date_column] AS date) AS time_period,
    CAST((CAST(analyzed_table.[date_column] AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state], CAST(analyzed_table.[date_column] AS date), CAST(analyzed_table.[date_column] AS date)
ORDER BY level_1, level_2CAST(analyzed_table.[date_column] AS date)
Teradata 
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."date_column" AS DATE) AS time_period,
    CAST(CAST(analyzed_table."date_column" AS DATE) AS TIMESTAMP) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Trino 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

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

monthly partition sql aggregate expression on column

Check description

Verifies that a custom aggregated SQL expression (MIN, MAX, etc.) is not outside the expected range. Stores a separate data quality check result for each monthly partition.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
monthly_partition_sql_aggregate_expression_on_column Custom aggregated SQL expression within range custom_sql partitioned monthly Reasonableness sql_aggregated_expression between_floats

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the monthly partition sql aggregate expression on column data quality check.

Managing monthly partition sql aggregate expression on column check from DQOps shell

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 -col=column_name -ch=monthly_partition_sql_aggregate_expression_on_column --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_* -col=column_name -ch=monthly_partition_sql_aggregate_expression_on_column --enable-warning

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=monthly_partition_sql_aggregate_expression_on_column --enable-warning
                    -Wfrom=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 -col=column_name -ch=monthly_partition_sql_aggregate_expression_on_column --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_* -col=column_name -ch=monthly_partition_sql_aggregate_expression_on_column --enable-error

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=monthly_partition_sql_aggregate_expression_on_column --enable-error
                    -Efrom=value

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

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

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

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

You can also run this check on all tables (and columns) on which the monthly_partition_sql_aggregate_expression_on_column check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=monthly_partition_sql_aggregate_expression_on_column

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:
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  columns:
    target_column:
      partitioned_checks:
        monthly:
          custom_sql:
            monthly_partition_sql_aggregate_expression_on_column:
              parameters:
                sql_expression: "MAX({column})"
              error:
                from: 10.0
                to: 20.5
      labels:
      - This is the column that is analyzed for data quality issues
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"
Samples of generated SQL queries for each data source type

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

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
ClickHouse 
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)) AS time_period,
    toDateTime64(DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)), 3) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Databricks 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
DB2 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE))) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
DuckDB 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM  AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
HANA 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN) AS time_period,
    TO_TIMESTAMP(SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
MariaDB 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
MySQL 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Oracle 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
PostgreSQL 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Presto 
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
    CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
QuestDB 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    CAST(DATE_TRUNC('month', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('month', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Redshift 
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Snowflake 
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Spark 
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
SQL Server 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.[target_column])) AS actual_value,
    DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1) AS time_period,
    CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[date_column]), 0)
ORDER BY DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)
Teradata 
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS time_period,
    CAST(TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS TIMESTAMP) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Trino 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
    CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

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

Configuration with data grouping

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  columns:
    target_column:
      partitioned_checks:
        monthly:
          custom_sql:
            monthly_partition_sql_aggregate_expression_on_column:
              parameters:
                sql_expression: "MAX({column})"
              error:
                from: 10.0
                to: 20.5
      labels:
      - This is the column that is analyzed for data quality issues
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

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

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ClickHouse 
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)) AS time_period,
    toDateTime64(DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)), 3) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Databricks 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
DB2 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE))) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
DuckDB 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM  AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
HANA 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN) AS time_period,
    TO_TIMESTAMP(SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
MariaDB 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
MySQL 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Oracle 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
PostgreSQL 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Presto 
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
    CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
QuestDB 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(DATE_TRUNC('month', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('month', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Redshift 
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Snowflake 
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Spark 
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.`target_column`)) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
SQL Server 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table.[target_column])) AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1) AS time_period,
    CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state], DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[date_column]), 0)
ORDER BY level_1, level_2DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)
Teradata 
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS time_period,
    CAST(TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS TIMESTAMP) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Trino 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    ({{ parameters.sql_expression | replace('{column}', lib.render_target_column('analyzed_table')) |
        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    (MAX(analyzed_table."target_column")) AS actual_value,

                analyzed_table.grouping_level_1,

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

What's next