date match format percent

date match format percent checks

Description
Column check that calculates the percentage of values that match the date format in a column.

---

profile date match format percent

Check description
Verifies that the percentage of date values matching the given format in a column does not exceed the minimum accepted percentage.

Check name	Check type	Time scale	Quality dimension	Sensor definition	Quality rule
profile_date_match_format_percent	profiling		Validity	date_match_format_percent	min_percent

Enable check (Shell)
To enable this check provide connection name and check name in check enable command

dqo> check enable -c=connection_name -ch=profile_date_match_format_percent

Run check (Shell)
To run this check provide check name in check run command

dqo> check run -ch=profile_date_match_format_percent

It is also possible to run this check on a specific connection. In order to do this, add the connection name to the below

dqo> check run -c=connection_name -ch=profile_date_match_format_percent

It is additionally feasible to run this check on a specific table. In order to do this, add the table name to the below

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

It is furthermore viable to combine run this check on a specific column. In order to do this, add the column name to the below

dqo> check run -c=connection_name -t=schema_name.table_name -col=column_name -ch=profile_date_match_format_percent

Check structure (YAML)

      profiling_checks:
        datetime:
          profile_date_match_format_percent:
            parameters:
              date_formats: DD/MM/YYYY
            warning:
              min_percent: 100.0
            error:
              min_percent: 99.0
            fatal:
              min_percent: 95.0

Sample configuration (YAML)
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  columns:
    target_column:
      profiling_checks:
        datetime:
          profile_date_match_format_percent:
            parameters:
              date_formats: DD/MM/YYYY
            warning:
              min_percent: 100.0
            error:
              min_percent: 99.0
            fatal:
              min_percent: 95.0
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$"
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$"
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$"
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        "^(\d{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$"
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST({{ lib.render_target_column('analyzed_table') }} AS STRING), r{{render_date_formats(parameters.date_formats)}}) IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST(analyzed_table.`target_column` AS STRING), r"^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$") IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table.`target_column`, '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

  {% macro render_date_formats(date_formats) %}
  {%- if date_formats == 'DD/MM/YYYY'-%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
  {%- elif date_formats == 'DD-MM-YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
  {%- elif date_formats == 'DD.MM.YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
  {%- elif date_formats == 'YYYY-MM-DD' -%}
  '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
  {%- endif -%}
  {% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])-(0[1-9]|1[0-2])-(\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1]).(0[1-9]|1[0-2]).(\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^(\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST({{lib.render_target_column('analyzed_table')}} AS VARCHAR) ~ {{render_date_formats(parameters.date_formats)}} IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table."target_column" AS VARCHAR) ~ '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$' IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{lib.render_target_column('analyzed_table')}} ~ {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" ~ '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])-(0[1-9]|1[0-2])-([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])\.(0[1-9]|1[0-2])\.([1-9][0-9]{3})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9][0-9]{3})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} LIKE {{render_date_formats(parameters.date_formats)}} ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] LIKE '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$' ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0) AS time_period,
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

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

Configuration with data grouping

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  columns:
    target_column:
      profiling_checks:
        datetime:
          profile_date_match_format_percent:
            parameters:
              date_formats: DD/MM/YYYY
            warning:
              min_percent: 100.0
            error:
              min_percent: 99.0
            fatal:
              min_percent: 95.0
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$"
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$"
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$"
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        "^(\d{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$"
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST({{ lib.render_target_column('analyzed_table') }} AS STRING), r{{render_date_formats(parameters.date_formats)}}) IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST(analyzed_table.`target_column` AS STRING), r"^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$") IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table.`target_column`, '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

  {% macro render_date_formats(date_formats) %}
  {%- if date_formats == 'DD/MM/YYYY'-%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
  {%- elif date_formats == 'DD-MM-YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
  {%- elif date_formats == 'DD.MM.YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
  {%- elif date_formats == 'YYYY-MM-DD' -%}
  '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
  {%- endif -%}
  {% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])-(0[1-9]|1[0-2])-(\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1]).(0[1-9]|1[0-2]).(\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^(\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST({{lib.render_target_column('analyzed_table')}} AS VARCHAR) ~ {{render_date_formats(parameters.date_formats)}} IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table."target_column" AS VARCHAR) ~ '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$' IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{lib.render_target_column('analyzed_table')}} ~ {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" ~ '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])-(0[1-9]|1[0-2])-([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])\.(0[1-9]|1[0-2])\.([1-9][0-9]{3})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9][0-9]{3})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} LIKE {{render_date_formats(parameters.date_formats)}} ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] LIKE '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$' ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0) AS time_period,
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 

---

daily date match format percent

Check description
Verifies that the percentage of date values matching the given format in a column does not exceed the maximum accepted percentage. Creates a separate data quality check (and an alert) for each daily monitoring.

Check name	Check type	Time scale	Quality dimension	Sensor definition	Quality rule
daily_date_match_format_percent	monitoring	daily	Validity	date_match_format_percent	min_percent

Enable check (Shell)
To enable this check provide connection name and check name in check enable command

dqo> check enable -c=connection_name -ch=daily_date_match_format_percent

Run check (Shell)
To run this check provide check name in check run command

dqo> check run -ch=daily_date_match_format_percent

It is also possible to run this check on a specific connection. In order to do this, add the connection name to the below

dqo> check run -c=connection_name -ch=daily_date_match_format_percent

It is additionally feasible to run this check on a specific table. In order to do this, add the table name to the below

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

It is furthermore viable to combine run this check on a specific column. In order to do this, add the column name to the below

dqo> check run -c=connection_name -t=schema_name.table_name -col=column_name -ch=daily_date_match_format_percent

Check structure (YAML)

      monitoring_checks:
        daily:
          datetime:
            daily_date_match_format_percent:
              parameters:
                date_formats: DD/MM/YYYY
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0

Sample configuration (YAML)
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  columns:
    target_column:
      monitoring_checks:
        daily:
          datetime:
            daily_date_match_format_percent:
              parameters:
                date_formats: DD/MM/YYYY
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$"
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$"
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$"
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        "^(\d{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$"
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST({{ lib.render_target_column('analyzed_table') }} AS STRING), r{{render_date_formats(parameters.date_formats)}}) IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST(analyzed_table.`target_column` AS STRING), r"^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$") IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(CURRENT_TIMESTAMP() AS DATE) AS time_period,
    TIMESTAMP(CAST(CURRENT_TIMESTAMP() AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table.`target_column`, '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

  {% macro render_date_formats(date_formats) %}
  {%- if date_formats == 'DD/MM/YYYY'-%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
  {%- elif date_formats == 'DD-MM-YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
  {%- elif date_formats == 'DD.MM.YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
  {%- elif date_formats == 'YYYY-MM-DD' -%}
  '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
  {%- endif -%}
  {% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(CURRENT_TIMESTAMP AS DATE)) AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])-(0[1-9]|1[0-2])-(\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1]).(0[1-9]|1[0-2]).(\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^(\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST({{lib.render_target_column('analyzed_table')}} AS VARCHAR) ~ {{render_date_formats(parameters.date_formats)}} IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table."target_column" AS VARCHAR) ~ '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$' IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{lib.render_target_column('analyzed_table')}} ~ {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" ~ '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date) AS time_period,
    TO_TIMESTAMP(CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])-(0[1-9]|1[0-2])-([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])\.(0[1-9]|1[0-2])\.([1-9][0-9]{3})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9][0-9]{3})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} LIKE {{render_date_formats(parameters.date_formats)}} ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] LIKE '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$' ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(SYSDATETIMEOFFSET() AS date) AS time_period,
    CAST((CAST(SYSDATETIMEOFFSET() AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

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

Configuration with data grouping

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  columns:
    target_column:
      monitoring_checks:
        daily:
          datetime:
            daily_date_match_format_percent:
              parameters:
                date_formats: DD/MM/YYYY
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$"
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$"
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$"
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        "^(\d{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$"
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST({{ lib.render_target_column('analyzed_table') }} AS STRING), r{{render_date_formats(parameters.date_formats)}}) IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST(analyzed_table.`target_column` AS STRING), r"^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$") IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    CAST(CURRENT_TIMESTAMP() AS DATE) AS time_period,
    TIMESTAMP(CAST(CURRENT_TIMESTAMP() AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table.`target_column`, '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

  {% macro render_date_formats(date_formats) %}
  {%- if date_formats == 'DD/MM/YYYY'-%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
  {%- elif date_formats == 'DD-MM-YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
  {%- elif date_formats == 'DD.MM.YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
  {%- elif date_formats == 'YYYY-MM-DD' -%}
  '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
  {%- endif -%}
  {% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    TRUNC(CAST(CURRENT_TIMESTAMP AS DATE)) AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])-(0[1-9]|1[0-2])-(\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1]).(0[1-9]|1[0-2]).(\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^(\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST({{lib.render_target_column('analyzed_table')}} AS VARCHAR) ~ {{render_date_formats(parameters.date_formats)}} IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table."target_column" AS VARCHAR) ~ '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$' IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{lib.render_target_column('analyzed_table')}} ~ {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" ~ '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date) AS time_period,
    TO_TIMESTAMP(CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])-(0[1-9]|1[0-2])-([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])\.(0[1-9]|1[0-2])\.([1-9][0-9]{3})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9][0-9]{3})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} LIKE {{render_date_formats(parameters.date_formats)}} ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] LIKE '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$' ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    CAST(SYSDATETIMEOFFSET() AS date) AS time_period,
    CAST((CAST(SYSDATETIMEOFFSET() AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 

---

monthly date match format percent

Check description
Verifies that the percentage of date values matching the given format in a column does not exceed the maximum accepted percentage. Creates a separate data quality check (and an alert) for each monthly monitoring.

Check name	Check type	Time scale	Quality dimension	Sensor definition	Quality rule
monthly_date_match_format_percent	monitoring	monthly	Validity	date_match_format_percent	min_percent

Enable check (Shell)
To enable this check provide connection name and check name in check enable command

dqo> check enable -c=connection_name -ch=monthly_date_match_format_percent

Run check (Shell)
To run this check provide check name in check run command

dqo> check run -ch=monthly_date_match_format_percent

It is also possible to run this check on a specific connection. In order to do this, add the connection name to the below

dqo> check run -c=connection_name -ch=monthly_date_match_format_percent

It is additionally feasible to run this check on a specific table. In order to do this, add the table name to the below

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

It is furthermore viable to combine run this check on a specific column. In order to do this, add the column name to the below

dqo> check run -c=connection_name -t=schema_name.table_name -col=column_name -ch=monthly_date_match_format_percent

Check structure (YAML)

      monitoring_checks:
        monthly:
          datetime:
            monthly_date_match_format_percent:
              parameters:
                date_formats: DD/MM/YYYY
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0

Sample configuration (YAML)
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  columns:
    target_column:
      monitoring_checks:
        monthly:
          datetime:
            monthly_date_match_format_percent:
              parameters:
                date_formats: DD/MM/YYYY
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$"
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$"
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$"
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        "^(\d{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$"
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST({{ lib.render_target_column('analyzed_table') }} AS STRING), r{{render_date_formats(parameters.date_formats)}}) IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST(analyzed_table.`target_column` AS STRING), r"^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$") IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table.`target_column`, '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

  {% macro render_date_formats(date_formats) %}
  {%- if date_formats == 'DD/MM/YYYY'-%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
  {%- elif date_formats == 'DD-MM-YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
  {%- elif date_formats == 'DD.MM.YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
  {%- elif date_formats == 'YYYY-MM-DD' -%}
  '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
  {%- endif -%}
  {% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])-(0[1-9]|1[0-2])-(\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1]).(0[1-9]|1[0-2]).(\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^(\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST({{lib.render_target_column('analyzed_table')}} AS VARCHAR) ~ {{render_date_formats(parameters.date_formats)}} IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table."target_column" AS VARCHAR) ~ '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$' IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{lib.render_target_column('analyzed_table')}} ~ {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" ~ '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])-(0[1-9]|1[0-2])-([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])\.(0[1-9]|1[0-2])\.([1-9][0-9]{3})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9][0-9]{3})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} LIKE {{render_date_formats(parameters.date_formats)}} ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] LIKE '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$' ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0) AS time_period,
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

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

Configuration with data grouping

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  columns:
    target_column:
      monitoring_checks:
        monthly:
          datetime:
            monthly_date_match_format_percent:
              parameters:
                date_formats: DD/MM/YYYY
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$"
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$"
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$"
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        "^(\d{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$"
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST({{ lib.render_target_column('analyzed_table') }} AS STRING), r{{render_date_formats(parameters.date_formats)}}) IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST(analyzed_table.`target_column` AS STRING), r"^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$") IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table.`target_column`, '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

  {% macro render_date_formats(date_formats) %}
  {%- if date_formats == 'DD/MM/YYYY'-%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
  {%- elif date_formats == 'DD-MM-YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
  {%- elif date_formats == 'DD.MM.YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
  {%- elif date_formats == 'YYYY-MM-DD' -%}
  '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
  {%- endif -%}
  {% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])-(0[1-9]|1[0-2])-(\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1]).(0[1-9]|1[0-2]).(\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^(\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST({{lib.render_target_column('analyzed_table')}} AS VARCHAR) ~ {{render_date_formats(parameters.date_formats)}} IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table."target_column" AS VARCHAR) ~ '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$' IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{lib.render_target_column('analyzed_table')}} ~ {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" ~ '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])-(0[1-9]|1[0-2])-([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])\.(0[1-9]|1[0-2])\.([1-9][0-9]{3})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9][0-9]{3})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} LIKE {{render_date_formats(parameters.date_formats)}} ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] LIKE '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$' ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0) AS time_period,
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 

---

daily partition date match format percent

Check description
Verifies that the percentage of date values matching the given format in a column does not exceed the maximum accepted percentage. Creates a separate data quality check (and an alert) for each daily partition.

Check name	Check type	Time scale	Quality dimension	Sensor definition	Quality rule
daily_partition_date_match_format_percent	partitioned	daily	Validity	date_match_format_percent	min_percent

Enable check (Shell)
To enable this check provide connection name and check name in check enable command

dqo> check enable -c=connection_name -ch=daily_partition_date_match_format_percent

Run check (Shell)
To run this check provide check name in check run command

dqo> check run -ch=daily_partition_date_match_format_percent

It is also possible to run this check on a specific connection. In order to do this, add the connection name to the below

dqo> check run -c=connection_name -ch=daily_partition_date_match_format_percent

It is additionally feasible to run this check on a specific table. In order to do this, add the table name to the below

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

It is furthermore viable to combine run this check on a specific column. In order to do this, add the column name to the below

dqo> check run -c=connection_name -t=schema_name.table_name -col=column_name -ch=daily_partition_date_match_format_percent

Check structure (YAML)

      partitioned_checks:
        daily:
          datetime:
            daily_partition_date_match_format_percent:
              parameters:
                date_formats: DD/MM/YYYY
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0

Sample configuration (YAML)
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  columns:
    target_column:
      partitioned_checks:
        daily:
          datetime:
            daily_partition_date_match_format_percent:
              parameters:
                date_formats: DD/MM/YYYY
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$"
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$"
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$"
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        "^(\d{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$"
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST({{ lib.render_target_column('analyzed_table') }} AS STRING), r{{render_date_formats(parameters.date_formats)}}) IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST(analyzed_table.`target_column` AS STRING), r"^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$") IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table.`target_column`, '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

  {% macro render_date_formats(date_formats) %}
  {%- if date_formats == 'DD/MM/YYYY'-%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
  {%- elif date_formats == 'DD-MM-YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
  {%- elif date_formats == 'DD.MM.YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
  {%- elif date_formats == 'YYYY-MM-DD' -%}
  '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
  {%- endif -%}
  {% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(original_table."date_column" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1])-(0[1-9]|1[0-2])-(\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^([0][1-9]|[1-2][0-9]|[3][0-1]).(0[1-9]|1[0-2]).(\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^(\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST({{lib.render_target_column('analyzed_table')}} AS VARCHAR) ~ {{render_date_formats(parameters.date_formats)}} IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table."target_column" AS VARCHAR) ~ '^([0][1-9]|[1-2][0-9]|[3][0-1])/(0[1-9]|1[0-2])/([0-9]{4})$' IS NOT NULL
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{lib.render_target_column('analyzed_table')}} ~ {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" ~ '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}


SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/]([1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9])$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(analyzed_table."date_column" AS date) AS time_period,
    TO_TIMESTAMP(CAST(analyzed_table."date_column" AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])-(0[1-9]|1[0-2])-([1-9][0-9]{3})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1-2][0-9]|3[0-1])\.(0[1-9]|1[0-2])\.([1-9][0-9]{3})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([1-9][0-9]{3})-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1])$'
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} LIKE {{render_date_formats(parameters.date_formats)}} ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] LIKE '^(0[1-9]|[1-2][0-9]|3[0-1])/(0[1-9]|1[0-2])/([1-9][0-9]{3})$' ESCAPE '~'
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(analyzed_table.[date_column] AS date) AS time_period,
    CAST((CAST(analyzed_table.[date_column] AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY CAST(analyzed_table.[date_column] AS date), CAST(analyzed_table.[date_column] AS date)
ORDER BY CAST(analyzed_table.[date_column] AS date)

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

Configuration with data grouping

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

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  columns:
    target_column:
      partitioned_checks:
        daily:
          datetime:
            daily_partition_date_match_format_percent:
              parameters:
                date_formats: DD/MM/YYYY
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

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

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$"
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$"
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        "^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$"
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        "^(\d{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$"
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST({{ lib.render_target_column('analyzed_table') }} AS STRING), r{{render_date_formats(parameters.date_formats)}}) IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST(analyzed_table.`target_column` AS STRING), r"^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$") IS NOT FALSE
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    CAST(analyzed_table.`date_column` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_formats(date_formats) %}
    {%- if date_formats == 'DD/MM/YYYY'-%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
    {%- elif date_formats == 'DD-MM-YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
    {%- elif date_formats == 'DD.MM.YYYY' -%}
        '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
    {%- elif date_formats == 'YYYY-MM-DD' -%}
        '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table.`target_column`, '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

  {% macro render_date_formats(date_formats) %}
  {%- if date_formats == 'DD/MM/YYYY'-%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$'
  {%- elif date_formats == 'DD-MM-YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[-](0[1-9]|1[0-2])[-](\d{4})$'
  {%- elif date_formats == 'DD.MM.YYYY' -%}
  '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[.](0[1-9]|1[0-2])[.](\d{4})$'
  {%- elif date_formats == 'YYYY-MM-DD' -%}
  '^([0-9]{4})[-](0[1-9]|1[0-2])[-](0[1-9]|[1][0-9]|[2][0-9]|3[01])$'
  {%- endif -%}
  {% endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE({{ lib.render_target_column('analyzed_table') }}, {{render_date_formats(parameters.date_formats)}})
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(CASE
              WHEN REGEXP_LIKE(analyzed_table."target_column", '^(0[1-9]|[1][0-9]|[2][0-9]|3[01])[/](0[1-9]|1[0-2])[/](\d{4})$')
                 THEN 1
              ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    TRUNC(CAST(original_table."date_column" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

PostgreSQL