Last updated: July 22, 2025

Sql invalid value count on column data quality checks, SQL examples

A column-level check that uses a custom SQL query that return invalid values from column. This check is used for setting testing queries or ready queries used by users in their own systems (legacy SQL queries). Use the {table} token to reference the tested table, and the {column} to reference the column that is tested. For example, when this check is applied on a column. The condition can find invalid values in the column which have values lower than 18 using an SQL query: `SELECT {column} FROM {table} WHERE {column} < 18`.

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

profile sql invalid value count on column

Check description

Runs a custom query that retrieves invalid values found in a column and returns the number of them, and raises an issue if too many failures were detected. This check is used for setting testing queries or ready queries used by users in their own systems (legacy SQL queries). For example, when this check is applied on a column. The condition can find invalid values in the column which have values lower than 18 using an SQL query: `SELECT {column} FROM {table} WHERE {column} < 18`.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
`profile_sql_invalid_value_count_on_column`	Custom SELECT SQL that returns invalid values	custom_sql	profiling		Validity	sql_invalid_value_count	max_count

Command-line examples

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

Managing profile sql invalid value count on column 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_sql_invalid_value_count_on_column --enable-warning

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

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

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=profile_sql_invalid_value_count_on_column --enable-warning
                    -Wmax_count=value

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

dqo> check activate -c=connection_name -t=schema_name.table_name -col=column_name -ch=profile_sql_invalid_value_count_on_column --enable-error

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

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

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

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

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

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

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

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

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

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

YAML configuration

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  columns:
    target_column:
      profiling_checks:
        custom_sql:
          profile_sql_invalid_value_count_on_column:
            parameters:
              sql_query: |-
                SELECT age AS actual_value
                FROM customers
                WHERE age < 18
            warning:
              max_count: 0
            error:
              max_count: 10
            fatal:
              max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues

Samples of generated SQL queries for each data source type

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) analyzed_table

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

daily sql invalid value count on column

Check description

Runs a custom query that retrieves invalid values found in a column and returns the number of them, and raises an issue if too many failures were detected. This check is used for setting testing queries or ready queries used by users in their own systems (legacy SQL queries). For example, when this check is applied on a column. The condition can find invalid values in the column which have values lower than 18 using an SQL query: `SELECT {column} FROM {table} WHERE {column} < 18`.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
`daily_sql_invalid_value_count_on_column`	Custom SELECT SQL that returns invalid values	custom_sql	monitoring	daily	Validity	sql_invalid_value_count	max_count

Command-line examples

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

Managing daily sql invalid value count on column 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_sql_invalid_value_count_on_column --enable-warning

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

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

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_sql_invalid_value_count_on_column --enable-warning
                    -Wmax_count=value

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

dqo> check activate -c=connection_name -t=schema_name.table_name -col=column_name -ch=daily_sql_invalid_value_count_on_column --enable-error

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

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

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

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

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

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

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

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

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

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

YAML configuration

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  columns:
    target_column:
      monitoring_checks:
        daily:
          custom_sql:
            daily_sql_invalid_value_count_on_column:
              parameters:
                sql_query: |-
                  SELECT age AS actual_value
                  FROM customers
                  WHERE age < 18
              warning:
                max_count: 0
              error:
                max_count: 10
              fatal:
                max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues

Samples of generated SQL queries for each data source type

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) analyzed_table

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

monthly sql invalid value count on column

Check description

Runs a custom query that retrieves invalid values found in a column and returns the number of them, and raises an issue if too many failures were detected. This check is used for setting testing queries or ready queries used by users in their own systems (legacy SQL queries). For example, when this check is applied on a column. The condition can find invalid values in the column which have values lower than 18 using an SQL query: `SELECT {column} FROM {table} WHERE {column} < 18`.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
`monthly_sql_invalid_value_count_on_column`	Custom SELECT SQL that returns invalid values	custom_sql	monitoring	monthly	Validity	sql_invalid_value_count	max_count

Command-line examples

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

Managing monthly sql invalid value count on column 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_sql_invalid_value_count_on_column --enable-warning

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

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

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=monthly_sql_invalid_value_count_on_column --enable-warning
                    -Wmax_count=value

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

dqo> check activate -c=connection_name -t=schema_name.table_name -col=column_name -ch=monthly_sql_invalid_value_count_on_column --enable-error

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

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

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

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

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

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

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

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

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

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

YAML configuration

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  columns:
    target_column:
      monitoring_checks:
        monthly:
          custom_sql:
            monthly_sql_invalid_value_count_on_column:
              parameters:
                sql_query: |-
                  SELECT age AS actual_value
                  FROM customers
                  WHERE age < 18
              warning:
                max_count: 0
              error:
                max_count: 10
              fatal:
                max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues

Samples of generated SQL queries for each data source type

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) analyzed_table

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) AS analyzed_table

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    COUNT(*) as actual_value
FROM (
    {{ parameters.sql_query | replace('{column}', lib.render_target_column('analyzed_table')) | replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }}
) AS analyzed_table

SELECT
    COUNT(*) as actual_value
FROM (
    SELECT age AS actual_value
FROM customers
WHERE age < 18
) 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