sum match

sum match checks

Description
Column level check that ensures that compares the sum of the values in the tested column to the sum of values in a reference column from the reference table. Compares the sum of values for each group of data. The data is grouped using a GROUP BY clause and groups are matched between the tested (parent) table and the reference table (the source of truth).

---

profile sum match

Check description
Verifies that percentage of the difference between the sum of values in a tested column in a parent table and the sum of a values in a column in the reference table. The difference must be below defined percentage thresholds.

Check name	Check type	Time scale	Sensor definition	Quality rule
profile_sum_match	profiling		sum	diff_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_sum_match

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

dqo> check run -ch=profile_sum_match

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_sum_match

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_sum_match

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_sum_match

Check structure (Yaml)

      profiling_checks:
        comparisons:
          compare_to_source_of_truth_table:
            reference_column: source_of_truth_column_name
            profile_sum_match:
              warning:
                max_diff_percent: 0.0
              error:
                max_diff_percent: 1.0
              fatal:
                max_diff_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
  table_comparisons:
    compare_to_source_of_truth_table:
      reference_table_connection_name: <source_of_truth_connection_name>
      reference_table_schema_name: <source_of_truth_schema_name>
      reference_table_name: <source_of_truth_table_name>
      check_type: profiling
      grouping_columns:
      - compared_table_column_name: country
        reference_table_column_name: country_column_name_on_reference_table
      - compared_table_column_name: state
        reference_table_column_name: state_column_name_on_reference_table
  columns:
    target_column:
      profiling_checks:
        comparisons:
          compare_to_source_of_truth_table:
            reference_column: source_of_truth_column_name
            profile_sum_match:
              warning:
                max_diff_percent: 0.0
              error:
                max_diff_percent: 1.0
              fatal:
                max_diff_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 for calculating aggregated values used
        for the table comparison
    state:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.`target_column`) 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.`target_column`) 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.[target_column]) 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

---

daily sum match

Check description
Verifies that percentage of the difference between the sum of values in a tested column in a parent table and the sum of a values in a column in the reference table. The difference must be below defined percentage thresholds. 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_sum_match	recurring	daily	sum	diff_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_sum_match

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

dqo> check run -ch=daily_sum_match

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_sum_match

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_sum_match

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_sum_match

Check structure (Yaml)

      recurring_checks:
        daily:
          comparisons:
            compare_to_source_of_truth_table:
              reference_column: source_of_truth_column_name
              daily_sum_match:
                warning:
                  max_diff_percent: 0.0
                error:
                  max_diff_percent: 1.0
                fatal:
                  max_diff_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
  table_comparisons:
    compare_to_source_of_truth_table:
      reference_table_connection_name: <source_of_truth_connection_name>
      reference_table_schema_name: <source_of_truth_schema_name>
      reference_table_name: <source_of_truth_table_name>
      check_type: profiling
      grouping_columns:
      - compared_table_column_name: country
        reference_table_column_name: country_column_name_on_reference_table
      - compared_table_column_name: state
        reference_table_column_name: state_column_name_on_reference_table
  columns:
    target_column:
      recurring_checks:
        daily:
          comparisons:
            compare_to_source_of_truth_table:
              reference_column: source_of_truth_column_name
              daily_sum_match:
                warning:
                  max_diff_percent: 0.0
                error:
                  max_diff_percent: 1.0
                fatal:
                  max_diff_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 for calculating aggregated values used
        for the table comparison
    state:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.`target_column`) 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.`target_column`) 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.[target_column]) 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

---

monthly sum match

Check description
Verifies that percentage of the difference between the sum of values in a tested column in a parent table and the sum of a values in a column in the reference table. The difference must be below defined percentage thresholds. Stores the most recent captured value for each month when the data quality check was evaluated.

Check name	Check type	Time scale	Sensor definition	Quality rule
monthly_sum_match	recurring	monthly	sum	diff_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_sum_match

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

dqo> check run -ch=monthly_sum_match

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_sum_match

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_sum_match

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_sum_match

Check structure (Yaml)

      recurring_checks:
        monthly:
          comparisons:
            compare_to_source_of_truth_table:
              reference_column: source_of_truth_column_name
              monthly_sum_match:
                warning:
                  max_diff_percent: 0.0
                error:
                  max_diff_percent: 1.0
                fatal:
                  max_diff_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
  table_comparisons:
    compare_to_source_of_truth_table:
      reference_table_connection_name: <source_of_truth_connection_name>
      reference_table_schema_name: <source_of_truth_schema_name>
      reference_table_name: <source_of_truth_table_name>
      check_type: profiling
      grouping_columns:
      - compared_table_column_name: country
        reference_table_column_name: country_column_name_on_reference_table
      - compared_table_column_name: state
        reference_table_column_name: state_column_name_on_reference_table
  columns:
    target_column:
      recurring_checks:
        monthly:
          comparisons:
            compare_to_source_of_truth_table:
              reference_column: source_of_truth_column_name
              monthly_sum_match:
                warning:
                  max_diff_percent: 0.0
                error:
                  max_diff_percent: 1.0
                fatal:
                  max_diff_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 for calculating aggregated values used
        for the table comparison
    state:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.`target_column`) 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.`target_column`) 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.[target_column]) 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

---

daily partition sum match

Check description
Verifies that percentage of the difference between the sum of values in a tested column in a parent table and the sum of a values in a column in the reference table. The difference must be below defined percentage thresholds. Compares each daily partition (each day of data) between the compared table and the reference table (the source of truth).

Check name	Check type	Time scale	Sensor definition	Quality rule
daily_partition_sum_match	partitioned	daily	sum	diff_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_sum_match

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

dqo> check run -ch=daily_partition_sum_match

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_sum_match

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_sum_match

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_sum_match

Check structure (Yaml)

      partitioned_checks:
        daily:
          comparisons:
            compare_to_source_of_truth_table:
              reference_column: source_of_truth_column_name
              daily_partition_sum_match:
                warning:
                  max_diff_percent: 0.0
                error:
                  max_diff_percent: 1.0
                fatal:
                  max_diff_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
  table_comparisons:
    compare_to_source_of_truth_table:
      reference_table_connection_name: <source_of_truth_connection_name>
      reference_table_schema_name: <source_of_truth_schema_name>
      reference_table_name: <source_of_truth_table_name>
      check_type: profiling
      grouping_columns:
      - compared_table_column_name: country
        reference_table_column_name: country_column_name_on_reference_table
      - compared_table_column_name: state
        reference_table_column_name: state_column_name_on_reference_table
  columns:
    target_column:
      partitioned_checks:
        daily:
          comparisons:
            compare_to_source_of_truth_table:
              reference_column: source_of_truth_column_name
              daily_partition_sum_match:
                warning:
                  max_diff_percent: 0.0
                error:
                  max_diff_percent: 1.0
                fatal:
                  max_diff_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 for calculating aggregated values used
        for the table comparison
    state:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.`target_column`) 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.`target_column`) 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.[target_column]) AS actual_value,
    CAST(analyzed_table.[] AS date) AS time_period,
    CAST((CAST(analyzed_table.[] AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY CAST(analyzed_table.[] AS date), CAST(analyzed_table.[] AS date)
ORDER BY CAST(analyzed_table.[] AS date)

---

monthly partition sum match

Check description
Verifies that percentage of the difference between the sum of values in a tested column in a parent table and the sum of a values in a column in the reference table. The difference must be below defined percentage thresholds. Compares each monthly partition (each month of data) between the compared table and the reference table (the source of truth).

Check name	Check type	Time scale	Sensor definition	Quality rule
monthly_partition_sum_match	partitioned	monthly	sum	diff_percent

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

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

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

dqo> check run -ch=monthly_partition_sum_match

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

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

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

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

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

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

Check structure (Yaml)

      partitioned_checks:
        monthly:
          comparisons:
            compare_to_source_of_truth_table:
              reference_column: source_of_truth_column_name
              monthly_partition_sum_match:
                warning:
                  max_diff_percent: 0.0
                error:
                  max_diff_percent: 1.0
                fatal:
                  max_diff_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
  table_comparisons:
    compare_to_source_of_truth_table:
      reference_table_connection_name: <source_of_truth_connection_name>
      reference_table_schema_name: <source_of_truth_schema_name>
      reference_table_name: <source_of_truth_table_name>
      check_type: profiling
      grouping_columns:
      - compared_table_column_name: country
        reference_table_column_name: country_column_name_on_reference_table
      - compared_table_column_name: state
        reference_table_column_name: state_column_name_on_reference_table
  columns:
    target_column:
      partitioned_checks:
        monthly:
          comparisons:
            compare_to_source_of_truth_table:
              reference_column: source_of_truth_column_name
              monthly_partition_sum_match:
                warning:
                  max_diff_percent: 0.0
                error:
                  max_diff_percent: 1.0
                fatal:
                  max_diff_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 for calculating aggregated values used
        for the table comparison
    state:
      labels:
      - column used as the first grouping key for calculating aggregated values used
        for the table comparison

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.`target_column`) AS actual_value,
    DATE_TRUNC(CAST(analyzed_table.`` AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`` AS DATE), MONTH)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.`target_column`) AS actual_value,
    DATE_FORMAT(analyzed_table.``, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.``, '%Y-%m-01 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") AS actual_value,
    time_period,
    time_period_utc
 FROM(
     SELECT
         original_table.*,
    TRUNC(CAST(original_table."" AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(original_table."" 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 -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table."target_column") AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    SUM({{ lib.render_target_column('analyzed_table')}}) 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
    SUM(analyzed_table.[target_column]) AS actual_value,
    DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1) AS time_period,
    CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[]), 0)
ORDER BY DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1)

---