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Last updated: July 22, 2025

Row count match data quality checks, SQL examples

Table level comparison check that compares the row count of the current (parent) table with the row count of the reference table.

The row count match data quality check has the following variants for each type of data quality checks supported by DQOps.

profile row count match

Check description

Verifies that the row count of the tested (parent) table matches the row count of the reference table. Compares each group of data with a GROUP BY clause.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
profile_row_count_match Maximum percentage of difference between row count of compared tables comparisons profiling Accuracy row_count diff_percent

Command-line examples

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

Managing profile row count match 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  -ch=profile_row_count_match --enable-warning

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

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

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

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

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

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

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

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

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

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

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

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

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_row_count_match

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

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

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:
  table_comparisons:
    compare_to_source_of_truth_table:
      reference_table_connection_name: <source_of_truth_connection_name>
      reference_table_schema_name: <source_of_truth_schema_name>
      reference_table_name: <source_of_truth_table_name>
      check_type: profiling
      grouping_columns:
      - compared_table_column_name: country
        reference_table_column_name: country_column_name_on_reference_table
      - compared_table_column_name: state
        reference_table_column_name: state_column_name_on_reference_table
  profiling_checks:
    comparisons:
      compare_to_source_of_truth_table:
        profile_row_count_match:
          warning:
            max_diff_percent: 0.0
          error:
            max_diff_percent: 1.0
          fatal:
            max_diff_percent: 5.0
  columns:
    country:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison
    state:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison
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 row_count data quality sensor.

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SELECT
    COUNT(*) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Databricks 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
DB2 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_table
DuckDB 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM  AS analyzed_table
HANA 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_table
MariaDB 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM `<target_table>` AS analyzed_table
MySQL 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM `<target_table>` AS analyzed_table
Oracle 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_table
PostgreSQL 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "your_trino_database"."<target_schema>"."<target_table>" analyzed_table) grouping_table
QuestDB 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT() 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
    COUNT() 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

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

SELECT
    COUNT(*) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
SQL Server 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    COUNT_BIG(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SELECT
    COUNT(*) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Trino 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" analyzed_table) grouping_table

daily row count match

Check description

Verifies that the row count of the tested (parent) table matches the row count of the reference table. Compares each group of data with a GROUP BY clause. 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_row_count_match Maximum percentage of difference between row count of compared tables comparisons monitoring daily Accuracy row_count diff_percent

Command-line examples

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

Managing daily row count match 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  -ch=daily_row_count_match --enable-warning

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

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

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

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

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

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

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

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

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

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

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

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

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_row_count_match

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

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

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:
  table_comparisons:
    compare_to_source_of_truth_table:
      reference_table_connection_name: <source_of_truth_connection_name>
      reference_table_schema_name: <source_of_truth_schema_name>
      reference_table_name: <source_of_truth_table_name>
      check_type: profiling
      grouping_columns:
      - compared_table_column_name: country
        reference_table_column_name: country_column_name_on_reference_table
      - compared_table_column_name: state
        reference_table_column_name: state_column_name_on_reference_table
  monitoring_checks:
    daily:
      comparisons:
        compare_to_source_of_truth_table:
          daily_row_count_match:
            warning:
              max_diff_percent: 0.0
            error:
              max_diff_percent: 1.0
            fatal:
              max_diff_percent: 5.0
  columns:
    country:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison
    state:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison
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 row_count data quality sensor.

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SELECT
    COUNT(*) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Databricks 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
DB2 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_table
DuckDB 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM  AS analyzed_table
HANA 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_table
MariaDB 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM `<target_table>` AS analyzed_table
MySQL 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM `<target_table>` AS analyzed_table
Oracle 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_table
PostgreSQL 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "your_trino_database"."<target_schema>"."<target_table>" analyzed_table) grouping_table
QuestDB 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT() 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
    COUNT() 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

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

SELECT
    COUNT(*) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
SQL Server 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    COUNT_BIG(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SELECT
    COUNT(*) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Trino 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" analyzed_table) grouping_table

monthly row count match

Check description

Verifies that the row count of the tested (parent) table matches the row count of the reference table. Compares each group of data with a GROUP BY clause. Stores the most recent captured value 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_row_count_match Maximum percentage of difference between row count of compared tables comparisons monitoring monthly Accuracy row_count diff_percent

Command-line examples

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

Managing monthly row count match 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  -ch=monthly_row_count_match --enable-warning

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

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

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

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

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

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

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

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

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

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

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

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

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_row_count_match

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

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

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:
  table_comparisons:
    compare_to_source_of_truth_table:
      reference_table_connection_name: <source_of_truth_connection_name>
      reference_table_schema_name: <source_of_truth_schema_name>
      reference_table_name: <source_of_truth_table_name>
      check_type: profiling
      grouping_columns:
      - compared_table_column_name: country
        reference_table_column_name: country_column_name_on_reference_table
      - compared_table_column_name: state
        reference_table_column_name: state_column_name_on_reference_table
  monitoring_checks:
    monthly:
      comparisons:
        compare_to_source_of_truth_table:
          monthly_row_count_match:
            warning:
              max_diff_percent: 0.0
            error:
              max_diff_percent: 1.0
            fatal:
              max_diff_percent: 5.0
  columns:
    country:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison
    state:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison
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 row_count data quality sensor.

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SELECT
    COUNT(*) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Databricks 
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
DB2 
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_table
DuckDB 
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM  AS analyzed_table
HANA 
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_table
MariaDB 
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM `<target_table>` AS analyzed_table
MySQL 
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM `<target_table>` AS analyzed_table
Oracle 
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_table
PostgreSQL 
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "your_trino_database"."<target_schema>"."<target_table>" analyzed_table) grouping_table
QuestDB 
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT() 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
    COUNT() 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

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

SELECT
    COUNT(*) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
SQL Server 
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    COUNT_BIG(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SELECT
    COUNT(*) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Trino 
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value
FROM (
    SELECT 1 AS actual_value
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" analyzed_table) grouping_table

daily partition row count match

Check description

Verifies that the row count of the tested (parent) table matches the row count of the reference table. Compares each group of data with a GROUP BY clause on the time period (the daily partition) and all other data grouping columns. Stores the most recent captured value for each daily partition that was analyzed.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
daily_partition_row_count_match Maximum percentage of difference between row count of compared tables comparisons partitioned daily Accuracy row_count diff_percent

Command-line examples

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

Managing daily partition row count match 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  -ch=daily_partition_row_count_match --enable-warning

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

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

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

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

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

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

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

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

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

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

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

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

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_row_count_match

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

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

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
  table_comparisons:
    compare_to_source_of_truth_table:
      reference_table_connection_name: <source_of_truth_connection_name>
      reference_table_schema_name: <source_of_truth_schema_name>
      reference_table_name: <source_of_truth_table_name>
      check_type: profiling
      grouping_columns:
      - compared_table_column_name: country
        reference_table_column_name: country_column_name_on_reference_table
      - compared_table_column_name: state
        reference_table_column_name: state_column_name_on_reference_table
  partitioned_checks:
    daily:
      comparisons:
        compare_to_source_of_truth_table:
          daily_partition_row_count_match:
            warning:
              max_diff_percent: 0.0
            error:
              max_diff_percent: 1.0
            fatal:
              max_diff_percent: 5.0
  columns:
    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 for calculating aggregated values used
        for the table comparison
    state:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison
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 row_count data quality sensor.

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

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

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT 1 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>" analyzed_table) grouping_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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT 1 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 "<target_schema>"."<target_table>" analyzed_table) grouping_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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT 1 AS actual_value,
    TRUNC(CAST(analyzed_table."date_column" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(analyzed_table."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT 1 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 "your_trino_database"."<target_schema>"."<target_table>" analyzed_table) grouping_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
    COUNT() 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
    COUNT() 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

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

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

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

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT 1 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 "your_trino_catalog"."<target_schema>"."<target_table>" analyzed_table) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

monthly partition row count match

Check description

Verifies that the row count of the tested (parent) table matches the row count of the reference table, for each monthly partition (grouping rows by the time period, truncated to the month). Compares each group of data with a GROUP BY clause. Stores the most recent captured value for each monthly partition and optionally data groups.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
monthly_partition_row_count_match Maximum percentage of difference between row count of compared tables comparisons partitioned monthly Accuracy row_count diff_percent

Command-line examples

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

Managing monthly partition row count match 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  -ch=monthly_partition_row_count_match --enable-warning

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

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

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

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

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

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

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

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

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

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

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

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

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_row_count_match

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

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

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
  table_comparisons:
    compare_to_source_of_truth_table:
      reference_table_connection_name: <source_of_truth_connection_name>
      reference_table_schema_name: <source_of_truth_schema_name>
      reference_table_name: <source_of_truth_table_name>
      check_type: profiling
      grouping_columns:
      - compared_table_column_name: country
        reference_table_column_name: country_column_name_on_reference_table
      - compared_table_column_name: state
        reference_table_column_name: state_column_name_on_reference_table
  partitioned_checks:
    monthly:
      comparisons:
        compare_to_source_of_truth_table:
          monthly_partition_row_count_match:
            warning:
              max_diff_percent: 0.0
            error:
              max_diff_percent: 1.0
            fatal:
              max_diff_percent: 5.0
  columns:
    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 for calculating aggregated values used
        for the table comparison
    state:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison
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 row_count data quality sensor.

BigQuery 
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

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

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT 1 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>" analyzed_table) grouping_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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT 1 AS actual_value,
    SERIES_ROUND(CAST(analyzed_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN) AS time_period,
    TO_TIMESTAMP(SERIES_ROUND(CAST(analyzed_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT 1 AS actual_value,
    TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" analyzed_table) grouping_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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT 1 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) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" analyzed_table) grouping_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
    COUNT() 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
    COUNT() 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

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

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

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

SELECT
    COUNT(*) 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
    COUNT(*) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT 1 AS actual_value
        {{- lib.render_data_grouping_projections('analyzed_table') }}
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} analyzed_table
    {{- lib.render_where_clause() -}}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COUNT(*) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT 1 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) AS time_period_utc
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" analyzed_table) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

What's next