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sql condition passed percent on column

sql condition passed percent on column checks

Description
Column level check that ensures that a set percentage of rows passed a custom SQL condition (expression).

profile sql condition passed percent on column

Check description
Verifies that a minimum percentage of rows passed a custom SQL condition (expression).

Check name Check type Time scale Sensor definition Quality rule
profile_sql_condition_passed_percent_on_column profiling sql_condition_passed_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_sql_condition_passed_percent_on_column
Run check (Shell)
To run this check provide check name in check run command 
dqo> check run -ch=profile_sql_condition_passed_percent_on_column
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_sql_condition_passed_percent_on_column
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=table_name -ch=profile_sql_condition_passed_percent_on_column
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=table_name -col=column_name -ch=profile_sql_condition_passed_percent_on_column
Check structure (Yaml) 
      profiling_checks:
        sql:
          profile_sql_condition_passed_percent_on_column:
            parameters:
              sql_condition: "{column} + col_tax = col_total_price_with_tax"
            warning:
              min_percent: 100.0
            error:
              min_percent: 99.0
            fatal:
              min_percent: 95.0
Sample configuration (Yaml)
# yaml-language-server: $schema=https://cloud.dqo.ai/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:
        sql:
          profile_sql_condition_passed_percent_on_column:
            parameters:
              sql_condition: "{column} + col_tax = col_total_price_with_tax"
            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

BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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

PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    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_BIG(analyzed_table.[target_column]) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] IS NOT NULL
                AND (analyzed_table.[target_column] + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG(analyzed_table.[target_column])
    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

Configuration with data grouping

Click to see more

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/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:
        sql:
          profile_sql_condition_passed_percent_on_column:
            parameters:
              sql_condition: "{column} + col_tax = col_total_price_with_tax"
            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
BigQuery 

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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 

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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

PostgreSQL 

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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 

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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 

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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 

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    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_BIG(analyzed_table.[target_column]) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] IS NOT NULL
                AND (analyzed_table.[target_column] + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG(analyzed_table.[target_column])
    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 sql condition passed percent on column

Check description
Verifies that a minimum percentage of rows passed a custom SQL condition (expression). Stores the most recent captured value for each day when the data quality check was evaluated.

Check name Check type Time scale Sensor definition Quality rule
daily_sql_condition_passed_percent_on_column recurring daily sql_condition_passed_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_sql_condition_passed_percent_on_column
Run check (Shell)
To run this check provide check name in check run command 
dqo> check run -ch=daily_sql_condition_passed_percent_on_column
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_sql_condition_passed_percent_on_column
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=table_name -ch=daily_sql_condition_passed_percent_on_column
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=table_name -col=column_name -ch=daily_sql_condition_passed_percent_on_column
Check structure (Yaml) 
      recurring_checks:
        daily:
          sql:
            daily_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0
Sample configuration (Yaml)
# yaml-language-server: $schema=https://cloud.dqo.ai/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:
      recurring_checks:
        daily:
          sql:
            daily_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              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

BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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

PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    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_BIG(analyzed_table.[target_column]) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] IS NOT NULL
                AND (analyzed_table.[target_column] + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG(analyzed_table.[target_column])
    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

Configuration with data grouping

Click to see more

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/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:
      recurring_checks:
        daily:
          sql:
            daily_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              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
BigQuery 

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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 

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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

PostgreSQL 

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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 

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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 

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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 

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    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_BIG(analyzed_table.[target_column]) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] IS NOT NULL
                AND (analyzed_table.[target_column] + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG(analyzed_table.[target_column])
    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 sql condition passed percent on column

Check description
Verifies that a minimum percentage of rows passed a custom SQL condition (expression). Stores the most recent row count for each month when the data quality check was evaluated.

Check name Check type Time scale Sensor definition Quality rule
monthly_sql_condition_passed_percent_on_column recurring monthly sql_condition_passed_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_sql_condition_passed_percent_on_column
Run check (Shell)
To run this check provide check name in check run command 
dqo> check run -ch=monthly_sql_condition_passed_percent_on_column
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_sql_condition_passed_percent_on_column
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=table_name -ch=monthly_sql_condition_passed_percent_on_column
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=table_name -col=column_name -ch=monthly_sql_condition_passed_percent_on_column
Check structure (Yaml) 
      recurring_checks:
        monthly:
          sql:
            monthly_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0
Sample configuration (Yaml)
# yaml-language-server: $schema=https://cloud.dqo.ai/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:
      recurring_checks:
        monthly:
          sql:
            monthly_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              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

BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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

PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    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_BIG(analyzed_table.[target_column]) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] IS NOT NULL
                AND (analyzed_table.[target_column] + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG(analyzed_table.[target_column])
    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

Configuration with data grouping

Click to see more

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/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:
      recurring_checks:
        monthly:
          sql:
            monthly_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              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
BigQuery 

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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 

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    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

PostgreSQL 

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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 

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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 

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    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 

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    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_BIG(analyzed_table.[target_column]) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] IS NOT NULL
                AND (analyzed_table.[target_column] + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG(analyzed_table.[target_column])
    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 sql condition passed percent on column

Check description
Verifies that a minimum percentage of rows passed a custom SQL condition (expression). Creates a separate data quality check (and an alert) for each daily partition.

Check name Check type Time scale Sensor definition Quality rule
daily_partition_sql_condition_passed_percent_on_column partitioned daily sql_condition_passed_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_sql_condition_passed_percent_on_column
Run check (Shell)
To run this check provide check name in check run command 
dqo> check run -ch=daily_partition_sql_condition_passed_percent_on_column
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_sql_condition_passed_percent_on_column
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=table_name -ch=daily_partition_sql_condition_passed_percent_on_column
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=table_name -col=column_name -ch=daily_partition_sql_condition_passed_percent_on_column
Check structure (Yaml) 
      partitioned_checks:
        daily:
          sql:
            daily_partition_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0
Sample configuration (Yaml)
# yaml-language-server: $schema=https://cloud.dqo.ai/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:
      partitioned_checks:
        daily:
          sql:
            daily_partition_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              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

BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    END AS actual_value,
    CAST(analyzed_table.`` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`` 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

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    END AS actual_value,
    DATE_FORMAT(analyzed_table.``, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.``, '%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

PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    END AS actual_value,
    CAST(analyzed_table."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" 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

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    END AS actual_value,
    CAST(analyzed_table."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    END AS actual_value,
    CAST(analyzed_table."" AS date) AS time_period,
    TO_TIMESTAMP(CAST(analyzed_table."" 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

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    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_BIG(analyzed_table.[target_column]) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] IS NOT NULL
                AND (analyzed_table.[target_column] + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG(analyzed_table.[target_column])
    END AS actual_value,
    CAST(analyzed_table.[] AS date) AS time_period,
    CAST((CAST(analyzed_table.[] 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.[] AS date), CAST(analyzed_table.[] AS date)
ORDER BY CAST(analyzed_table.[] AS date)

Configuration with data grouping

Click to see more

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/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:
      partitioned_checks:
        daily:
          sql:
            daily_partition_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              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
BigQuery 

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    CAST(analyzed_table.`` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`` 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 

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.``, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.``, '%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

PostgreSQL 

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" 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 

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake 

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."" AS date) AS time_period,
    TO_TIMESTAMP(CAST(analyzed_table."" 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 

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    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_BIG(analyzed_table.[target_column]) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] IS NOT NULL
                AND (analyzed_table.[target_column] + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG(analyzed_table.[target_column])
    END AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    CAST(analyzed_table.[] AS date) AS time_period,
    CAST((CAST(analyzed_table.[] AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state], CAST(analyzed_table.[] AS date), CAST(analyzed_table.[] AS date)
ORDER BY level_1, level_2CAST(analyzed_table.[] AS date)

monthly partition sql condition passed percent on column

Check description
Verifies that a minimum percentage of rows passed a custom SQL condition (expression). Creates a separate data quality check (and an alert) for each monthly partition.

Check name Check type Time scale Sensor definition Quality rule
monthly_partition_sql_condition_passed_percent_on_column partitioned monthly sql_condition_passed_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_partition_sql_condition_passed_percent_on_column
Run check (Shell)
To run this check provide check name in check run command 
dqo> check run -ch=monthly_partition_sql_condition_passed_percent_on_column
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_partition_sql_condition_passed_percent_on_column
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=table_name -ch=monthly_partition_sql_condition_passed_percent_on_column
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=table_name -col=column_name -ch=monthly_partition_sql_condition_passed_percent_on_column
Check structure (Yaml) 
      partitioned_checks:
        monthly:
          sql:
            monthly_partition_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              warning:
                min_percent: 100.0
              error:
                min_percent: 99.0
              fatal:
                min_percent: 95.0
Sample configuration (Yaml)
# yaml-language-server: $schema=https://cloud.dqo.ai/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:
      partitioned_checks:
        monthly:
          sql:
            monthly_partition_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              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

BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    END AS actual_value,
    DATE_TRUNC(CAST(analyzed_table.`` AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`` 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

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    END AS actual_value,
    DATE_FORMAT(analyzed_table.``, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.``, '%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

PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."" 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

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."" 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

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    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_BIG(analyzed_table.[target_column]) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.[target_column] IS NOT NULL
                AND (analyzed_table.[target_column] + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG(analyzed_table.[target_column])
    END AS actual_value,
    DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1) AS time_period,
    CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[]), 0)
ORDER BY DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1)

Configuration with data grouping

Click to see more

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/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:
      partitioned_checks:
        monthly:
          sql:
            monthly_partition_sql_condition_passed_percent_on_column:
              parameters:
                sql_condition: "{column} + col_tax = col_total_price_with_tax"
              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
BigQuery 

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC(CAST(analyzed_table.`` AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`` 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 

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table.`target_column` IS NOT NULL
                AND (analyzed_table.`target_column` + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table.`target_column`)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.``, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.``, '%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

PostgreSQL 

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."" 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 

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IS NOT NULL
                AND ({{ parameters.sql_condition | replace('{column}', lib.render_target_column('analyzed_table')) |
                        replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN analyzed_table."target_column" IS NOT NULL
                AND (analyzed_table."target_column" + col_tax = col_total_price_with_tax)
                    THEN 1
                ELSE 0
            END
        ) / COUNT(analyzed_table."target_column")
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state"