datetime value in range date percent

datetime value in range date percent checks

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

---

profile datetime value in range date percent

Check description
Verifies that the percentage of date values in the range defined by the user in a column does not exceed the maximum accepted percentage.

Check name	Check type	Time scale	Sensor definition	Quality rule
profile_datetime_value_in_range_date_percent	profiling		value_in_range_date_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_datetime_value_in_range_date_percent

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

dqo> check run -ch=profile_datetime_value_in_range_date_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_datetime_value_in_range_date_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_datetime_value_in_range_date_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_datetime_value_in_range_date_percent

Check structure (Yaml)

      profiling_checks:
        datetime:
          profile_datetime_value_in_range_date_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_datetime_value_in_range_date_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_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

{% macro render_ordering_column_names() %}
    {%- if lib.time_series is not none and lib.time_series.mode != 'current_time' -%}
        ORDER BY {{ lib.render_time_dimension_expression(lib.table_alias_prefix) }}
    {%- elif (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) %}
        {{ ', ' }}
    {% endif %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) -%}
        {%- for attribute in lib.data_groupings -%}
            {%- if not loop.first -%}
                {{ ', ' }}
            {%- endif -%}
                {{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- render_ordering_column_names() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATE) >= '' AND TRY_CAST(analyzed_table.[target_column] AS DATE) <= '' 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_datetime_value_in_range_date_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_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table."target_column" AS DATE) >= '' AND CAST(analyzed_table."target_column" AS DATE) <= '' THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,

                analyzed_table.grouping_level_1,

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

{% macro render_ordering_column_names() %}
    {%- if lib.time_series is not none and lib.time_series.mode != 'current_time' -%}
        ORDER BY {{ lib.render_time_dimension_expression(lib.table_alias_prefix) }}
    {%- elif (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) %}
        {{ ', ' }}
    {% endif %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) -%}
        {%- for attribute in lib.data_groupings -%}
            {%- if not loop.first -%}
                {{ ', ' }}
            {%- endif -%}
                {{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- render_ordering_column_names() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATE) >= '' AND TRY_CAST(analyzed_table.[target_column] AS DATE) <= '' 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]
        , 
    level_1, level_2

---

daily datetime value in range date percent

Check description
Verifies that the percentage of date values in the range defined by the user 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_datetime_value_in_range_date_percent	recurring	daily	value_in_range_date_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_datetime_value_in_range_date_percent

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

dqo> check run -ch=daily_datetime_value_in_range_date_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_datetime_value_in_range_date_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_datetime_value_in_range_date_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_datetime_value_in_range_date_percent

Check structure (Yaml)

      recurring_checks:
        daily:
          datetime:
            daily_datetime_value_in_range_date_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_datetime_value_in_range_date_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_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

{% macro render_ordering_column_names() %}
    {%- if lib.time_series is not none and lib.time_series.mode != 'current_time' -%}
        ORDER BY {{ lib.render_time_dimension_expression(lib.table_alias_prefix) }}
    {%- elif (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) %}
        {{ ', ' }}
    {% endif %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) -%}
        {%- for attribute in lib.data_groupings -%}
            {%- if not loop.first -%}
                {{ ', ' }}
            {%- endif -%}
                {{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- render_ordering_column_names() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATE) >= '' AND TRY_CAST(analyzed_table.[target_column] AS DATE) <= '' 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_datetime_value_in_range_date_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_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table."target_column" AS DATE) >= '' AND CAST(analyzed_table."target_column" AS DATE) <= '' THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,

                analyzed_table.grouping_level_1,

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

{% macro render_ordering_column_names() %}
    {%- if lib.time_series is not none and lib.time_series.mode != 'current_time' -%}
        ORDER BY {{ lib.render_time_dimension_expression(lib.table_alias_prefix) }}
    {%- elif (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) %}
        {{ ', ' }}
    {% endif %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) -%}
        {%- for attribute in lib.data_groupings -%}
            {%- if not loop.first -%}
                {{ ', ' }}
            {%- endif -%}
                {{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- render_ordering_column_names() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATE) >= '' AND TRY_CAST(analyzed_table.[target_column] AS DATE) <= '' 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]
        , 
    level_1, level_2

---

monthly datetime value in range date percent

Check description
Verifies that the percentage of date values in the range defined by the user 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_datetime_value_in_range_date_percent	recurring	monthly	value_in_range_date_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_datetime_value_in_range_date_percent

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

dqo> check run -ch=monthly_datetime_value_in_range_date_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_datetime_value_in_range_date_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_datetime_value_in_range_date_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_datetime_value_in_range_date_percent

Check structure (Yaml)

      recurring_checks:
        monthly:
          datetime:
            monthly_datetime_value_in_range_date_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_datetime_value_in_range_date_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_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

{% macro render_ordering_column_names() %}
    {%- if lib.time_series is not none and lib.time_series.mode != 'current_time' -%}
        ORDER BY {{ lib.render_time_dimension_expression(lib.table_alias_prefix) }}
    {%- elif (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) %}
        {{ ', ' }}
    {% endif %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) -%}
        {%- for attribute in lib.data_groupings -%}
            {%- if not loop.first -%}
                {{ ', ' }}
            {%- endif -%}
                {{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- render_ordering_column_names() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATE) >= '' AND TRY_CAST(analyzed_table.[target_column] AS DATE) <= '' 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_datetime_value_in_range_date_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_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table."target_column" AS DATE) >= '' AND CAST(analyzed_table."target_column" AS DATE) <= '' THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,

                analyzed_table.grouping_level_1,

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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

{% macro render_ordering_column_names() %}
    {%- if lib.time_series is not none and lib.time_series.mode != 'current_time' -%}
        ORDER BY {{ lib.render_time_dimension_expression(lib.table_alias_prefix) }}
    {%- elif (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) %}
        {{ ', ' }}
    {% endif %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) -%}
        {%- for attribute in lib.data_groupings -%}
            {%- if not loop.first -%}
                {{ ', ' }}
            {%- endif -%}
                {{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- render_ordering_column_names() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATE) >= '' AND TRY_CAST(analyzed_table.[target_column] AS DATE) <= '' 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]
        , 
    level_1, level_2

---

daily partition datetime value in range date percent

Check description
Verifies that the percentage of date values in the range defined by the user 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_datetime_value_in_range_date_percent	partitioned	daily	value_in_range_date_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_datetime_value_in_range_date_percent

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

dqo> check run -ch=daily_partition_datetime_value_in_range_date_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_datetime_value_in_range_date_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_datetime_value_in_range_date_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_datetime_value_in_range_date_percent

Check structure (Yaml)

      partitioned_checks:
        daily:
          datetime:
            daily_partition_datetime_value_in_range_date_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_datetime_value_in_range_date_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_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    SAFE_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN SAFE_CAST(analyzed_table.`target_column` AS DATE) >= '' AND SAFE_CAST(analyzed_table.`target_column` AS DATE) <= '' 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_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.`target_column`) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table.`target_column` AS DATE) >= '' AND CAST(analyzed_table.`target_column` AS DATE) <= '' 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_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table."target_column") = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN CAST(analyzed_table."target_column" AS DATE) >= '' AND CAST(analyzed_table."target_column" AS DATE) <= '' 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_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true'-%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

{% macro render_date_format_cast()%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_date_format_cast() -%}
    {%- if lib.is_local_date(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    {{ lib.render_target_column('analyzed_table') }}
    {%- elif lib.is_local_time(table.columns[column_name].type_snapshot.column_type) == 'true' or lib.is_instant(table.columns[column_name].type_snapshot.column_type) == 'true' -%}
    CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- else -%}
    TRY_CAST({{ lib.render_target_column('analyzed_table') }} AS DATE)
    {%- endif -%}
{%- endmacro -%}

{% macro render_ordering_column_names() %}
    {%- if lib.time_series is not none and lib.time_series.mode != 'current_time' -%}
        ORDER BY {{ lib.render_time_dimension_expression(lib.table_alias_prefix) }}
    {%- elif (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) %}
        {{ ', ' }}
    {% endif %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) -%}
        {%- for attribute in lib.data_groupings -%}
            {%- if not loop.first -%}
                {{ ', ' }}
            {%- endif -%}
                {{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{% endmacro %}

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ render_date_format_cast() }} >= {{ lib.make_text_constant(parameters.min_value) }} AND {{ render_date_format_cast() }} <= {{ lib.make_text_constant(parameters.max_value) }} THEN 1
            ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- render_ordering_column_names() -}}

SELECT
    CASE
        WHEN COUNT(analyzed_table.[target_column]) = 0 THEN NULL
        ELSE 100.0 * SUM(
            CASE
                WHEN TRY_CAST(analyzed_table.[target_column] AS DATE) >= '' AND TRY_CAST(analyzed_table.[target_column