Last updated: July 22, 2025

Total not null count match percent data quality checks, SQL examples

A column-level check that ensures that the difference between the count of null values in the tested column and the count of null values in another column in a referenced table is below a maximum accepted percentage of difference. This check runs an SQL query with an INNER JOIN clause to join another (referenced) table that must be defined in the same database.

The total not null count match percent data quality check has the following variants for each type of data quality checks supported by DQOps.

profile total not null count match percent

Check description

Verifies that the percentage of difference in total not null count of a column in a table and total not null count of a column of another table does not exceed the set number. 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
`profile_total_not_null_count_match_percent`	The maximum difference in percent between the count of not null values of this column and the reference column	accuracy	profiling		Accuracy	total_not_null_count_match_percent	diff_percent

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the profile total not null count match percent data quality check.

Managing profile total not null count match percent check from DQOps shell

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

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

dqo> check activate -c=connection_name -t=schema_name.table_name -col=column_name -ch=profile_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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 -col=column_name -ch=profile_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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_total_not_null_count_match_percent check on all tables and columns on a single data source.

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

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_total_not_null_count_match_percent

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

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

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:
        accuracy:
          profile_total_not_null_count_match_percent:
            parameters:
              referenced_table: landing_zone.customer_raw
              referenced_column: customer_id
            warning:
              max_diff_percent: 0.0
            error:
              max_diff_percent: 1.0
            fatal:
              max_diff_percent: 5.0
      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 total_not_null_count_match_percent data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_project_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ lib.render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

DB2

Sensor template for DB2Rendered SQL for DB2

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT({{ lib.render_target_column('original_table')}}) AS actual_value
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause() -}} ) analyzed_table
GROUP BY actual_value

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT(original_table."target_column") AS actual_value
    FROM "<target_schema>"."<target_table>" original_table) analyzed_table
GROUP BY actual_value

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

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

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ lib.render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM  AS analyzed_table

HANA

Sensor template for HANARendered SQL for HANA

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

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ lib.render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_table>` AS analyzed_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_table>` AS analyzed_table

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT({{ lib.render_target_column('original_table')}}) AS actual_value
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause() -}} ) analyzed_table
GROUP BY actual_value

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT(original_table."target_column") AS actual_value
    FROM "<target_schema>"."<target_table>" original_table) analyzed_table
GROUP BY actual_value

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table

Presto

Sensor template for PrestoRendered SQL for Presto

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

WITH referenced_data AS (
    SELECT COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }}) AS expected_value
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
)
SELECT referenced_data.expected_value AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
CROSS JOIN referenced_data
{{- lib.render_where_clause() -}}

WITH referenced_data AS (
    SELECT COUNT(referenced_table."customer_id") AS expected_value
    FROM landing_zone.customer_raw AS referenced_table
)
SELECT referenced_data.expected_value AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_table>" AS analyzed_table
CROSS JOIN referenced_data

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table

Spark

Sensor template for SparkRendered SQL for Spark

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT_BIG(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT_BIG({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT_BIG(referenced_table.[customer_id])
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT_BIG(analyzed_table.[target_column]) AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Trino

Sensor template for TrinoRendered SQL for Trino

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_catalog_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table

daily total not null count match percent

Check description

Verifies that the percentage of difference in total not null count of a column in a table and total not null count of a column of another table does not exceed the set number. 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_total_not_null_count_match_percent`	The maximum difference in percent between the count of not null values of this column and the reference column	accuracy	monitoring	daily	Accuracy	total_not_null_count_match_percent	diff_percent

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the daily total not null count match percent data quality check.

Managing daily total not null count match percent check from DQOps shell

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

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

dqo> check activate -c=connection_name -t=schema_name.table_name -col=column_name -ch=daily_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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 -col=column_name -ch=daily_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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_total_not_null_count_match_percent check on all tables and columns on a single data source.

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

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_total_not_null_count_match_percent

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

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

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:
          accuracy:
            daily_total_not_null_count_match_percent:
              parameters:
                referenced_table: landing_zone.customer_raw
                referenced_column: customer_id
              warning:
                max_diff_percent: 0.0
              error:
                max_diff_percent: 1.0
              fatal:
                max_diff_percent: 5.0
      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 total_not_null_count_match_percent data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_project_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ lib.render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

DB2

Sensor template for DB2Rendered SQL for DB2

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT({{ lib.render_target_column('original_table')}}) AS actual_value
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause() -}} ) analyzed_table
GROUP BY actual_value

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT(original_table."target_column") AS actual_value
    FROM "<target_schema>"."<target_table>" original_table) analyzed_table
GROUP BY actual_value

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

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

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ lib.render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM  AS analyzed_table

HANA

Sensor template for HANARendered SQL for HANA

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

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ lib.render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_table>` AS analyzed_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_table>` AS analyzed_table

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT({{ lib.render_target_column('original_table')}}) AS actual_value
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause() -}} ) analyzed_table
GROUP BY actual_value

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT(original_table."target_column") AS actual_value
    FROM "<target_schema>"."<target_table>" original_table) analyzed_table
GROUP BY actual_value

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table

Presto

Sensor template for PrestoRendered SQL for Presto

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

WITH referenced_data AS (
    SELECT COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }}) AS expected_value
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
)
SELECT referenced_data.expected_value AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
CROSS JOIN referenced_data
{{- lib.render_where_clause() -}}

WITH referenced_data AS (
    SELECT COUNT(referenced_table."customer_id") AS expected_value
    FROM landing_zone.customer_raw AS referenced_table
)
SELECT referenced_data.expected_value AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_table>" AS analyzed_table
CROSS JOIN referenced_data

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table

Spark

Sensor template for SparkRendered SQL for Spark

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT_BIG(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT_BIG({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT_BIG(referenced_table.[customer_id])
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT_BIG(analyzed_table.[target_column]) AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Trino

Sensor template for TrinoRendered SQL for Trino

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_catalog_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table

monthly total not null count match percent

Check description

Verifies that the percentage of difference in total not null count of a column in a table and total not null count of a column of another table does not exceed the set number. 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_total_not_null_count_match_percent`	The maximum difference in percent between the count of not null values of this column and the reference column	accuracy	monitoring	monthly	Accuracy	total_not_null_count_match_percent	diff_percent

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the monthly total not null count match percent data quality check.

Managing monthly total not null count match percent check from DQOps shell

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

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

dqo> check activate -c=connection_name -t=schema_name.table_name -col=column_name -ch=monthly_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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 -col=column_name -ch=monthly_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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_total_not_null_count_match_percent --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_total_not_null_count_match_percent check on all tables and columns on a single data source.

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

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_total_not_null_count_match_percent

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

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

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:
          accuracy:
            monthly_total_not_null_count_match_percent:
              parameters:
                referenced_table: landing_zone.customer_raw
                referenced_column: customer_id
              warning:
                max_diff_percent: 0.0
              error:
                max_diff_percent: 1.0
              fatal:
                max_diff_percent: 5.0
      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 total_not_null_count_match_percent data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_project_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ lib.render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

DB2

Sensor template for DB2Rendered SQL for DB2

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT({{ lib.render_target_column('original_table')}}) AS actual_value
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause() -}} ) analyzed_table
GROUP BY actual_value

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT(original_table."target_column") AS actual_value
    FROM "<target_schema>"."<target_table>" original_table) analyzed_table
GROUP BY actual_value

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

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

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ lib.render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM  AS analyzed_table

HANA

Sensor template for HANARendered SQL for HANA

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

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ lib.render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_table>` AS analyzed_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_table>` AS analyzed_table

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT({{ lib.render_target_column('original_table')}}) AS actual_value
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause() -}} ) analyzed_table
GROUP BY actual_value

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw referenced_table
    ) AS expected_value,
    analyzed_table.actual_value
FROM (SELECT
        COUNT(original_table."target_column") AS actual_value
    FROM "<target_schema>"."<target_table>" original_table) analyzed_table
GROUP BY actual_value

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table

Presto

Sensor template for PrestoRendered SQL for Presto

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

WITH referenced_data AS (
    SELECT COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }}) AS expected_value
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
)
SELECT referenced_data.expected_value AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
CROSS JOIN referenced_data
{{- lib.render_where_clause() -}}

WITH referenced_data AS (
    SELECT COUNT(referenced_table."customer_id") AS expected_value
    FROM landing_zone.customer_raw AS referenced_table
)
SELECT referenced_data.expected_value AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_table>" AS analyzed_table
CROSS JOIN referenced_data

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table

Spark

Sensor template for SparkRendered SQL for Spark

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table.`customer_id`)
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table.`target_column`) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_database_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT_BIG(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT_BIG({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT_BIG(referenced_table.[customer_id])
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT_BIG(analyzed_table.[target_column]) AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table

Trino

Sensor template for TrinoRendered SQL for Trino

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

{%- macro render_referenced_table(referenced_table) -%}
{%- if referenced_table.find(".") < 0 -%}
   {{ lib.quote_identifier(lib.macro_catalog_name) }}.{{ lib.quote_identifier(lib.macro_schema_name) }}.{{- lib.quote_identifier(referenced_table) -}}
{%- else -%}
   {{ referenced_table }}
{%- endif -%}
{%- endmacro -%}

SELECT
    (SELECT
        COUNT(referenced_table.{{ lib.quote_identifier(parameters.referenced_column) }})
    FROM {{ render_referenced_table(parameters.referenced_table) }} AS referenced_table
    ) AS expected_value,
    COUNT({{ lib.render_target_column('analyzed_table')}}) AS actual_value
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}

SELECT
    (SELECT
        COUNT(referenced_table."customer_id")
    FROM landing_zone.customer_raw AS referenced_table
    ) AS expected_value,
    COUNT(analyzed_table."target_column") AS actual_value
FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table

What's next

Learn how to configure data quality checks in DQOps
Look at the examples of running data quality checks, targeting tables and columns