date values in future percent

date values in future percent checks

Description
Column level check that ensures that there are no more than a set percentage of date values in future in a monitored column.

---

profile date values in future percent

Check description
Verifies that the percentage of date values in future in a column does not exceed the maximum accepted percentage.

Check name	Check type	Time scale	Sensor definition	Quality rule
profile_date_values_in_future_percent	profiling		date_values_in_future_percent	max_percent

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

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

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

dqo> check run -ch=profile_date_values_in_future_percent

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

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

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

dqo> check run -c=connection_name -t=table_name -ch=profile_date_values_in_future_percent

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

dqo> check run -c=connection_name -t=table_name -col=column_name -ch=profile_date_values_in_future_percent

Check structure (Yaml)

      profiling_checks:
        datetime:
          profile_date_values_in_future_percent:
            warning:
              max_percent: 1.0
            error:
              max_percent: 2.0
            fatal:
              max_percent: 5.0

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:
        datetime:
          profile_date_values_in_future_percent:
            warning:
              max_percent: 1.0
            error:
              max_percent: 2.0
            fatal:
              max_percent: 5.0
      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

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table."target_column" AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_typ) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_TO_TIMESTAMP({{ lib.render_target_column('analyzed_table') }}) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_TO_TIMESTAMP(analyzed_table."target_column") > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0) AS time_period,
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

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:
        datetime:
          profile_date_values_in_future_percent:
            warning:
              max_percent: 1.0
            error:
              max_percent: 2.0
            fatal:
              max_percent: 5.0
      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

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table."target_column" AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,

                analyzed_table.grouping_level_1,

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_typ) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_TO_TIMESTAMP({{ lib.render_target_column('analyzed_table') }}) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_TO_TIMESTAMP(analyzed_table."target_column") > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0) AS time_period,
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 

---

daily date values in future percent

Check description
Verifies that the percentage of date values in future in a column does not exceed the maximum accepted percentage. 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_date_values_in_future_percent	recurring	daily	date_values_in_future_percent	max_percent

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

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

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

dqo> check run -ch=daily_date_values_in_future_percent

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

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

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

dqo> check run -c=connection_name -t=table_name -ch=daily_date_values_in_future_percent

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

dqo> check run -c=connection_name -t=table_name -col=column_name -ch=daily_date_values_in_future_percent

Check structure (Yaml)

      recurring_checks:
        daily:
          datetime:
            daily_date_values_in_future_percent:
              warning:
                max_percent: 1.0
              error:
                max_percent: 2.0
              fatal:
                max_percent: 5.0

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:
          datetime:
            daily_date_values_in_future_percent:
              warning:
                max_percent: 1.0
              error:
                max_percent: 2.0
              fatal:
                max_percent: 5.0
      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

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(CURRENT_TIMESTAMP() AS DATE) AS time_period,
    TIMESTAMP(CAST(CURRENT_TIMESTAMP() AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table."target_column" AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(CURRENT_TIMESTAMP AS DATE)) AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_typ) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_TO_TIMESTAMP({{ lib.render_target_column('analyzed_table') }}) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_TO_TIMESTAMP(analyzed_table."target_column") > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date) AS time_period,
    TO_TIMESTAMP(CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CAST(SYSDATETIMEOFFSET() AS date) AS time_period,
    CAST((CAST(SYSDATETIMEOFFSET() AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

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:
          datetime:
            daily_date_values_in_future_percent:
              warning:
                max_percent: 1.0
              error:
                max_percent: 2.0
              fatal:
                max_percent: 5.0
      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

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    CAST(CURRENT_TIMESTAMP() AS DATE) AS time_period,
    TIMESTAMP(CAST(CURRENT_TIMESTAMP() AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table."target_column" AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,

                analyzed_table.grouping_level_1,

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_typ) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_TO_TIMESTAMP({{ lib.render_target_column('analyzed_table') }}) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_TO_TIMESTAMP(analyzed_table."target_column") > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date) AS time_period,
    TO_TIMESTAMP(CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    CAST(SYSDATETIMEOFFSET() AS date) AS time_period,
    CAST((CAST(SYSDATETIMEOFFSET() AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 

---

monthly date values in future percent

Check description
Verifies that the percentage of date values in future in a column does not exceed the maximum accepted percentage. 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_date_values_in_future_percent	recurring	monthly	date_values_in_future_percent	max_percent

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

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

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

dqo> check run -ch=monthly_date_values_in_future_percent

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

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

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

dqo> check run -c=connection_name -t=table_name -ch=monthly_date_values_in_future_percent

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

dqo> check run -c=connection_name -t=table_name -col=column_name -ch=monthly_date_values_in_future_percent

Check structure (Yaml)

      recurring_checks:
        monthly:
          datetime:
            monthly_date_values_in_future_percent:
              warning:
                max_percent: 1.0
              error:
                max_percent: 2.0
              fatal:
                max_percent: 5.0

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:
          datetime:
            monthly_date_values_in_future_percent:
              warning:
                max_percent: 1.0
              error:
                max_percent: 2.0
              fatal:
                max_percent: 5.0
      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

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table."target_column" AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_typ) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_TO_TIMESTAMP({{ lib.render_target_column('analyzed_table') }}) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_TO_TIMESTAMP(analyzed_table."target_column") > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0) AS time_period,
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table

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:
          datetime:
            monthly_date_values_in_future_percent:
              warning:
                max_percent: 1.0
              error:
                max_percent: 2.0
              fatal:
                max_percent: 5.0
      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

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(LOCALTIMESTAMP, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table."target_column" AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,

                analyzed_table.grouping_level_1,

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_typ) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN (analyzed_table."target_column")::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_TO_TIMESTAMP({{ lib.render_target_column('analyzed_table') }}) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_TO_TIMESTAMP(analyzed_table."target_column") > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > GETDATE()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATETIME) > SYSDATETIME()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0) AS time_period,
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 

---

daily partition date values in future percent

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

Check name	Check type	Time scale	Sensor definition	Quality rule
daily_partition_date_values_in_future_percent	partitioned	daily	date_values_in_future_percent	max_percent

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

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

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

dqo> check run -ch=daily_partition_date_values_in_future_percent

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

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

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

dqo> check run -c=connection_name -t=table_name -ch=daily_partition_date_values_in_future_percent

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

dqo> check run -c=connection_name -t=table_name -col=column_name -ch=daily_partition_date_values_in_future_percent

Check structure (Yaml)

      partitioned_checks:
        daily:
          datetime:
            daily_partition_date_values_in_future_percent:
              warning:
                max_percent: 1.0
              error:
                max_percent: 2.0
              fatal:
                max_percent: 5.0

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:
          datetime:
            daily_partition_date_values_in_future_percent:
              warning:
                max_percent: 1.0
              error:
                max_percent: 2.0
              fatal:
                max_percent: 5.0
      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

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    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

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE()
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME()
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS TIMESTAMP) > CURRENT_TIMESTAMP()
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            {{ render_value_in_future() }}
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table."target_column" AS TIMESTAMP) > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    TRUNC(CAST(original_table."" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(original_table."" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_value_in_future() -%}
    {%- if lib.is_instant(table.columns[column_name].type_snapshot.column_typ) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATE
                    THEN 1
                ELSE 0
            END
    {%- elif lib.is_local_date_time(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} > CURRENT_DATETIME
                    THEN 1
                ELSE 0
            END
    {%- else -%}
            CASE
                WHEN ({{ lib.render_target_column('analyzed_table') }})::TIMESTAMP > CURRENT_TIMESTAMP
                    THEN 1
                ELSE 0
            END