Last updated: July 22, 2025
Sql condition passed percent on table data quality checks, SQL examples
A table-level check that ensures that a minimum percentage of rows passed a custom SQL condition (expression). Measures the percentage of rows passing the condition. Raises a data quality issue when the percent of valid rows is below the min_percent parameter.
The sql condition passed percent on table data quality check has the following variants for each type of data quality checks supported by DQOps.
profile sql condition passed percent on table
Check description
Verifies that a custom SQL expression is met for each row. Counts the number of rows where the expression is not satisfied, and raises an issue if too many failures were detected. This check is used also to compare values between columns: `{alias}.col_price > {alias}.col_tax`.
Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
---|---|---|---|---|---|---|---|---|
profile_sql_condition_passed_percent_on_table |
Minimum percentage of rows that passed SQL condition | custom_sql | profiling | Validity | sql_condition_passed_percent | min_percent |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the profile sql condition passed percent on table data quality check.
Managing profile sql condition passed percent on table check from DQOps shell
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=profile_sql_condition_passed_percent_on_table --enable-warning
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=profile_sql_condition_passed_percent_on_table --enable-warning
Additional rule parameters are passed using the -Wrule_parameter_name=value.
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=profile_sql_condition_passed_percent_on_table --enable-error
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=profile_sql_condition_passed_percent_on_table --enable-error
Additional rule parameters are passed using the -Erule_parameter_name=value.
Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the profile_sql_condition_passed_percent_on_table check on all tables on a single data source.
It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.
dqo> check run -c=connection_name -t=schema_name.table_name -ch=profile_sql_condition_passed_percent_on_table
You can also run this check on all tables on which the profile_sql_condition_passed_percent_on_table check is enabled using patterns to find tables.
YAML configuration
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
profiling_checks:
custom_sql:
profile_sql_condition_passed_percent_on_table:
parameters:
sql_condition: SUM(col_total_impressions) > SUM(col_total_clicks)
warning:
min_percent: 100.0
error:
min_percent: 99.0
fatal:
min_percent: 95.0
columns: {}
Samples of generated SQL queries for each data source type
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the sql_condition_passed_percent data quality sensor.
BigQuery
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value
FROM (
SELECT
original_table.*
FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT_BIG(*)
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() -}}
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value
FROM (
SELECT
original_table.*
FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).
Configuration with data grouping
Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
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
profiling_checks:
custom_sql:
profile_sql_condition_passed_percent_on_table:
parameters:
sql_condition: SUM(col_total_impressions) > SUM(col_total_clicks)
warning:
min_percent: 100.0
error:
min_percent: 99.0
fatal:
min_percent: 95.0
columns:
country:
labels:
- column used as the first grouping key
state:
labels:
- column used as the second grouping key
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the sql_condition_passed_percent sensor.
BigQuery
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM (
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM (
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(), 100.0)
AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value
{{- lib.render_data_grouping_projections('analyzed_table') }}
{{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value,
analyzed_table.[country] AS grouping_level_1,
analyzed_table.[state] AS grouping_level_2
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
,
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM (
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
daily sql condition passed percent on table
Check description
Verifies that a minimum percentage of rows passed a custom SQL condition (expression). Reference the current table by using tokens, for example: `{alias}.col_price > {alias}.col_tax`. Stores the most recent captured percentage for each day when the data quality check was evaluated.
Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
---|---|---|---|---|---|---|---|---|
daily_sql_condition_passed_percent_on_table |
Minimum percentage of rows that passed SQL condition | custom_sql | monitoring | daily | Validity | sql_condition_passed_percent | min_percent |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the daily sql condition passed percent on table data quality check.
Managing daily sql condition passed percent on table check from DQOps shell
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=daily_sql_condition_passed_percent_on_table --enable-warning
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=daily_sql_condition_passed_percent_on_table --enable-warning
Additional rule parameters are passed using the -Wrule_parameter_name=value.
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=daily_sql_condition_passed_percent_on_table --enable-error
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=daily_sql_condition_passed_percent_on_table --enable-error
Additional rule parameters are passed using the -Erule_parameter_name=value.
Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the daily_sql_condition_passed_percent_on_table check on all tables on a single data source.
It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.
dqo> check run -c=connection_name -t=schema_name.table_name -ch=daily_sql_condition_passed_percent_on_table
You can also run this check on all tables on which the daily_sql_condition_passed_percent_on_table check is enabled using patterns to find tables.
YAML configuration
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
monitoring_checks:
daily:
custom_sql:
daily_sql_condition_passed_percent_on_table:
parameters:
sql_condition: SUM(col_total_impressions) > SUM(col_total_clicks)
warning:
min_percent: 100.0
error:
min_percent: 99.0
fatal:
min_percent: 95.0
columns: {}
Samples of generated SQL queries for each data source type
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the sql_condition_passed_percent data quality sensor.
BigQuery
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value
FROM (
SELECT
original_table.*
FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT_BIG(*)
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() -}}
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value
FROM (
SELECT
original_table.*
FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).
Configuration with data grouping
Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
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
monitoring_checks:
daily:
custom_sql:
daily_sql_condition_passed_percent_on_table:
parameters:
sql_condition: SUM(col_total_impressions) > SUM(col_total_clicks)
warning:
min_percent: 100.0
error:
min_percent: 99.0
fatal:
min_percent: 95.0
columns:
country:
labels:
- column used as the first grouping key
state:
labels:
- column used as the second grouping key
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the sql_condition_passed_percent sensor.
BigQuery
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM (
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM (
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(), 100.0)
AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value
{{- lib.render_data_grouping_projections('analyzed_table') }}
{{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value,
analyzed_table.[country] AS grouping_level_1,
analyzed_table.[state] AS grouping_level_2
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
,
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM (
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
monthly sql condition passed percent on table
Check description
Verifies that a minimum percentage of rows passed a custom SQL condition (expression). Reference the current table by using tokens, for example: `{alias}.col_price > {alias}.col_tax`. Stores the most recent value for each month when the data quality check was evaluated.
Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
---|---|---|---|---|---|---|---|---|
monthly_sql_condition_passed_percent_on_table |
Minimum percentage of rows that passed SQL condition | custom_sql | monitoring | monthly | Validity | sql_condition_passed_percent | min_percent |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the monthly sql condition passed percent on table data quality check.
Managing monthly sql condition passed percent on table check from DQOps shell
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=monthly_sql_condition_passed_percent_on_table --enable-warning
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=monthly_sql_condition_passed_percent_on_table --enable-warning
Additional rule parameters are passed using the -Wrule_parameter_name=value.
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=monthly_sql_condition_passed_percent_on_table --enable-error
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=monthly_sql_condition_passed_percent_on_table --enable-error
Additional rule parameters are passed using the -Erule_parameter_name=value.
Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the monthly_sql_condition_passed_percent_on_table check on all tables on a single data source.
It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.
dqo> check run -c=connection_name -t=schema_name.table_name -ch=monthly_sql_condition_passed_percent_on_table
You can also run this check on all tables on which the monthly_sql_condition_passed_percent_on_table check is enabled using patterns to find tables.
YAML configuration
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
monitoring_checks:
monthly:
custom_sql:
monthly_sql_condition_passed_percent_on_table:
parameters:
sql_condition: SUM(col_total_impressions) > SUM(col_total_clicks)
warning:
min_percent: 100.0
error:
min_percent: 99.0
fatal:
min_percent: 95.0
columns: {}
Samples of generated SQL queries for each data source type
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the sql_condition_passed_percent data quality sensor.
BigQuery
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value
FROM (
SELECT
original_table.*
FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT_BIG(*)
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() -}}
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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() -}}
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value
FROM (
SELECT
original_table.*
FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).
Configuration with data grouping
Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
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
monitoring_checks:
monthly:
custom_sql:
monthly_sql_condition_passed_percent_on_table:
parameters:
sql_condition: SUM(col_total_impressions) > SUM(col_total_clicks)
warning:
min_percent: 100.0
error:
min_percent: 99.0
fatal:
min_percent: 95.0
columns:
country:
labels:
- column used as the first grouping key
state:
labels:
- column used as the second grouping key
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the sql_condition_passed_percent sensor.
BigQuery
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM (
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM (
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(), 100.0)
AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value
{{- lib.render_data_grouping_projections('analyzed_table') }}
{{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value,
analyzed_table.[country] AS grouping_level_1,
analyzed_table.[state] AS grouping_level_2
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
,
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value,
analyzed_table.grouping_level_1,
analyzed_table.grouping_level_2
FROM (
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2
FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
daily partition sql condition passed percent on table
Check description
Verifies that a minimum percentage of rows passed a custom SQL condition (expression). Reference the current table by using tokens, for example: `{alias}.col_price > {alias}.col_tax`. Stores a separate data quality check result for each daily partition.
Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
---|---|---|---|---|---|---|---|---|
daily_partition_sql_condition_passed_percent_on_table |
Minimum percentage of rows that passed SQL condition | custom_sql | partitioned | daily | Validity | sql_condition_passed_percent | min_percent |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the daily partition sql condition passed percent on table data quality check.
Managing daily partition sql condition passed percent on table check from DQOps shell
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=daily_partition_sql_condition_passed_percent_on_table --enable-warning
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=daily_partition_sql_condition_passed_percent_on_table --enable-warning
Additional rule parameters are passed using the -Wrule_parameter_name=value.
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=daily_partition_sql_condition_passed_percent_on_table --enable-error
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=daily_partition_sql_condition_passed_percent_on_table --enable-error
Additional rule parameters are passed using the -Erule_parameter_name=value.
Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the daily_partition_sql_condition_passed_percent_on_table check on all tables on a single data source.
It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.
dqo> check run -c=connection_name -t=schema_name.table_name -ch=daily_partition_sql_condition_passed_percent_on_table
You can also run this check on all tables on which the daily_partition_sql_condition_passed_percent_on_table check is enabled using patterns to find tables.
YAML configuration
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
timestamp_columns:
partition_by_column: date_column
incremental_time_window:
daily_partitioning_recent_days: 7
monthly_partitioning_recent_months: 1
partitioned_checks:
daily:
custom_sql:
daily_partition_sql_condition_passed_percent_on_table:
parameters:
sql_condition: SUM(col_total_impressions) > SUM(col_total_clicks)
warning:
min_percent: 100.0
error:
min_percent: 99.0
fatal:
min_percent: 95.0
columns:
date_column:
labels:
- "date or datetime column used as a daily or monthly partitioning key, dates\
\ (and times) are truncated to a day or a month by the sensor's query for\
\ partitioned checks"
Samples of generated SQL queries for each data source type
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the sql_condition_passed_percent data quality sensor.
BigQuery
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
CAST(analyzed_table.`date_column` AS DATE) AS time_period,
TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
CAST(analyzed_table."date_column" AS DATE) AS time_period,
toDateTime64(CAST(analyzed_table."date_column" AS DATE), 3) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
CAST(analyzed_table.`date_column` AS DATE) AS time_period,
TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,time_period,
time_period_utc
FROM(
SELECT
original_table.*,
CAST(original_table."date_column" AS DATE) AS time_period,
TIMESTAMP(CAST(original_table."date_column" AS DATE)) 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
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
CAST(analyzed_table."date_column" AS date) AS time_period,
CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
time_period,
time_period_utc
FROM (
SELECT
original_table.*,
CAST(original_table."date_column" AS DATE) AS time_period,
TO_TIMESTAMP(CAST(original_table."date_column" AS DATE)) 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
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,time_period,
time_period_utc
FROM(
SELECT
original_table.*,
TRUNC(CAST(original_table."date_column" AS DATE)) AS time_period,
CAST(TRUNC(CAST(original_table."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
CAST(analyzed_table."date_column" AS date) AS time_period,
CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value,
time_period,
time_period_utc
FROM (
SELECT
original_table.*,
CAST(original_table."date_column" AS date) AS time_period,
CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(), 100.0)
AS actual_value,
time_period,
time_period_utc
FROM(
SELECT
original_table.*,
CAST(DATE_TRUNC('day', original_table."date_column") AS DATE) AS time_period,
CAST((CAST(DATE_TRUNC('day', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
CAST(analyzed_table."date_column" AS date) AS time_period,
CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
CAST(analyzed_table."date_column" AS date) AS time_period,
TO_TIMESTAMP(CAST(analyzed_table."date_column" AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
CAST(analyzed_table.`date_column` AS DATE) AS time_period,
TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value
{{- lib.render_data_grouping_projections('analyzed_table') }}
{{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value,
CAST(analyzed_table.[date_column] AS date) AS time_period,
CAST((CAST(analyzed_table.[date_column] AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY CAST(analyzed_table.[date_column] AS date), CAST(analyzed_table.[date_column] AS date)
ORDER BY CAST(analyzed_table.[date_column] AS date)
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
CAST(analyzed_table."date_column" AS DATE) AS time_period,
CAST(CAST(analyzed_table."date_column" AS DATE) AS TIMESTAMP) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value,
time_period,
time_period_utc
FROM (
SELECT
original_table.*,
CAST(original_table."date_column" AS date) AS time_period,
CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).
Configuration with data grouping
Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
timestamp_columns:
partition_by_column: date_column
incremental_time_window:
daily_partitioning_recent_days: 7
monthly_partitioning_recent_months: 1
default_grouping_name: group_by_country_and_state
groupings:
group_by_country_and_state:
level_1:
source: column_value
column: country
level_2:
source: column_value
column: state
partitioned_checks:
daily:
custom_sql:
daily_partition_sql_condition_passed_percent_on_table:
parameters:
sql_condition: SUM(col_total_impressions) > SUM(col_total_clicks)
warning:
min_percent: 100.0
error:
min_percent: 99.0
fatal:
min_percent: 95.0
columns:
date_column:
labels:
- "date or datetime column used as a daily or monthly partitioning key, dates\
\ (and times) are truncated to a day or a month by the sensor's query for\
\ partitioned checks"
country:
labels:
- column used as the first grouping key
state:
labels:
- column used as the second grouping key
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the sql_condition_passed_percent sensor.
BigQuery
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2,
CAST(analyzed_table.`date_column` AS DATE) AS time_period,
TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2,
CAST(analyzed_table."date_column" AS DATE) AS time_period,
toDateTime64(CAST(analyzed_table."date_column" AS DATE), 3) AS time_period_utc
FROM "<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
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2,
CAST(analyzed_table.`date_column` AS DATE) AS time_period,
TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<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
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,time_period,
time_period_utc
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2,
CAST(original_table."date_column" AS DATE) AS time_period,
TIMESTAMP(CAST(original_table."date_column" AS DATE)) 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
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2,
CAST(analyzed_table."date_column" AS date) AS time_period,
CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM 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
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
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,
CAST(original_table."date_column" AS DATE) AS time_period,
TO_TIMESTAMP(CAST(original_table."date_column" AS DATE)) 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
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2,
DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2,
DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,time_period,
time_period_utc
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2,
TRUNC(CAST(original_table."date_column" AS DATE)) AS time_period,
CAST(TRUNC(CAST(original_table."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2,
CAST(analyzed_table."date_column" AS date) AS time_period,
CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / 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,
CAST(original_table."date_column" AS date) AS time_period,
CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
FROM "your_trino_database"."<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
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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,
CAST(DATE_TRUNC('day', original_table."date_column") AS DATE) AS time_period,
CAST((CAST(DATE_TRUNC('day', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "<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
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2,
CAST(analyzed_table."date_column" AS date) AS time_period,
CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2,
CAST(analyzed_table."date_column" AS date) AS time_period,
TO_TIMESTAMP(CAST(analyzed_table."date_column" AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2,
CAST(analyzed_table.`date_column` AS DATE) AS time_period,
TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value
{{- lib.render_data_grouping_projections('analyzed_table') }}
{{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value,
analyzed_table.[country] AS grouping_level_1,
analyzed_table.[state] AS grouping_level_2,
CAST(analyzed_table.[date_column] AS date) AS time_period,
CAST((CAST(analyzed_table.[date_column] AS date)) AS DATETIME) AS time_period_utc
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
GROUP BY analyzed_table.[country], analyzed_table.[state], CAST(analyzed_table.[date_column] AS date), CAST(analyzed_table.[date_column] AS date)
ORDER BY level_1, level_2CAST(analyzed_table.[date_column] AS date)
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2,
CAST(analyzed_table."date_column" AS DATE) AS time_period,
CAST(CAST(analyzed_table."date_column" AS DATE) AS TIMESTAMP) AS time_period_utc
FROM "<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
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / 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,
CAST(original_table."date_column" AS date) AS time_period,
CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
FROM "your_trino_catalog"."<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
monthly partition sql condition passed percent on table
Check description
Verifies that a minimum percentage of rows passed a custom SQL condition (expression). Reference the current table by using tokens, for example: `{alias}.col_price > {alias}.col_tax`. Stores a separate data quality check result for each monthly partition.
Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
---|---|---|---|---|---|---|---|---|
monthly_partition_sql_condition_passed_percent_on_table |
Minimum percentage of rows that passed SQL condition | custom_sql | partitioned | monthly | Validity | sql_condition_passed_percent | min_percent |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the monthly partition sql condition passed percent on table data quality check.
Managing monthly partition sql condition passed percent on table check from DQOps shell
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=monthly_partition_sql_condition_passed_percent_on_table --enable-warning
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=monthly_partition_sql_condition_passed_percent_on_table --enable-warning
Additional rule parameters are passed using the -Wrule_parameter_name=value.
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=monthly_partition_sql_condition_passed_percent_on_table --enable-error
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=monthly_partition_sql_condition_passed_percent_on_table --enable-error
Additional rule parameters are passed using the -Erule_parameter_name=value.
Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the monthly_partition_sql_condition_passed_percent_on_table check on all tables on a single data source.
It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.
dqo> check run -c=connection_name -t=schema_name.table_name -ch=monthly_partition_sql_condition_passed_percent_on_table
You can also run this check on all tables on which the monthly_partition_sql_condition_passed_percent_on_table check is enabled using patterns to find tables.
YAML configuration
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
timestamp_columns:
partition_by_column: date_column
incremental_time_window:
daily_partitioning_recent_days: 7
monthly_partitioning_recent_months: 1
partitioned_checks:
monthly:
custom_sql:
monthly_partition_sql_condition_passed_percent_on_table:
parameters:
sql_condition: SUM(col_total_impressions) > SUM(col_total_clicks)
warning:
min_percent: 100.0
error:
min_percent: 99.0
fatal:
min_percent: 95.0
columns:
date_column:
labels:
- "date or datetime column used as a daily or monthly partitioning key, dates\
\ (and times) are truncated to a day or a month by the sensor's query for\
\ partitioned checks"
Samples of generated SQL queries for each data source type
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the sql_condition_passed_percent data quality sensor.
BigQuery
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH) AS time_period,
TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`date_column` 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
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)) AS time_period,
toDateTime64(DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)), 3) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,time_period,
time_period_utc
FROM(
SELECT
original_table.*,
DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE)) AS time_period,
TIMESTAMP(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE))) 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
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
time_period,
time_period_utc
FROM (
SELECT
original_table.*,
SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN) AS time_period,
TO_TIMESTAMP(SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN)) 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
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%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
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%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
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,time_period,
time_period_utc
FROM(
SELECT
original_table.*,
TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS time_period,
CAST(TRUNC(CAST(original_table."date_column" 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
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value,
time_period,
time_period_utc
FROM (
SELECT
original_table.*,
DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(), 100.0)
AS actual_value,
time_period,
time_period_utc
FROM(
SELECT
original_table.*,
CAST(DATE_TRUNC('month', original_table."date_column") AS DATE) AS time_period,
CAST((CAST(DATE_TRUNC('month', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value
{{- lib.render_data_grouping_projections('analyzed_table') }}
{{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value,
DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1) AS time_period,
CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] 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.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[date_column]), 0)
ORDER BY DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS time_period,
CAST(TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS TIMESTAMP) AS time_period_utc
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / COUNT(*)
END AS actual_value,
time_period,
time_period_utc
FROM (
SELECT
original_table.*,
DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).
Configuration with data grouping
Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
timestamp_columns:
partition_by_column: date_column
incremental_time_window:
daily_partitioning_recent_days: 7
monthly_partitioning_recent_months: 1
default_grouping_name: group_by_country_and_state
groupings:
group_by_country_and_state:
level_1:
source: column_value
column: country
level_2:
source: column_value
column: state
partitioned_checks:
monthly:
custom_sql:
monthly_partition_sql_condition_passed_percent_on_table:
parameters:
sql_condition: SUM(col_total_impressions) > SUM(col_total_clicks)
warning:
min_percent: 100.0
error:
min_percent: 99.0
fatal:
min_percent: 95.0
columns:
date_column:
labels:
- "date or datetime column used as a daily or monthly partitioning key, dates\
\ (and times) are truncated to a day or a month by the sensor's query for\
\ partitioned checks"
country:
labels:
- column used as the first grouping key
state:
labels:
- column used as the second grouping key
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the sql_condition_passed_percent sensor.
BigQuery
{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
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(analyzed_table.`date_column` AS DATE), MONTH) AS time_period,
TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`date_column` 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
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
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(analyzed_table."date_column" AS DATE)) AS time_period,
toDateTime64(DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)), 3) AS time_period_utc
FROM "<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
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
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(analyzed_table.`date_column` AS DATE)) AS time_period,
TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<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
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,time_period,
time_period_utc
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2,
DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE)) AS time_period,
TIMESTAMP(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE))) 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
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
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(analyzed_table."date_column" AS date)) AS time_period,
CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM 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
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
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,
SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN) AS time_period,
TO_TIMESTAMP(SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN)) 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
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2,
DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%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
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table.`country` AS grouping_level_1,
analyzed_table.`state` AS grouping_level_2,
DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%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
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value {{-"," if lib.time_series is not none else ""}}
{%- if lib.time_series is not none-%}
time_period,
time_period_utc
{% endif %}
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,time_period,
time_period_utc
FROM(
SELECT
original_table.*,
original_table."country" AS grouping_level_1,
original_table."state" AS grouping_level_2,
TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS time_period,
CAST(TRUNC(CAST(original_table."date_column" 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
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
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(analyzed_table."date_column" AS date)) AS time_period,
CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / 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,
DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
FROM "your_trino_database"."<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
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
COALESCE(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(), 100.0)
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,
CAST(DATE_TRUNC('month', original_table."date_column") AS DATE) AS time_period,
CAST((CAST(DATE_TRUNC('month', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "<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
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
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(analyzed_table."date_column" AS date)) AS time_period,
CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
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(analyzed_table."date_column" AS date)) AS time_period,
TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" 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
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
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(analyzed_table.`date_column` AS DATE)) AS time_period,
TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value
{{- lib.render_data_grouping_projections('analyzed_table') }}
{{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT_BIG(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT_BIG(*)
END AS actual_value,
analyzed_table.[country] AS grouping_level_1,
analyzed_table.[state] AS grouping_level_2,
DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1) AS time_period,
CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)) 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], DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[date_column]), 0)
ORDER BY level_1, level_2DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
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(*) = 0 THEN 100.0
ELSE 100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) / COUNT(*)
END AS actual_value,
analyzed_table."country" AS grouping_level_1,
analyzed_table."state" AS grouping_level_2,
TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS time_period,
CAST(TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS TIMESTAMP) AS time_period_utc
FROM "<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
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN ({{ parameters.sql_condition |
replace('{table}', lib.render_target_table()) | replace('{alias}', 'analyzed_table') }})
THEN 1
ELSE 0
END) AS DOUBLE) / 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
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
CASE
WHEN COUNT(*) = 0 THEN 100.0
ELSE CAST(100.0 * SUM(
CASE
WHEN (SUM(col_total_impressions) > SUM(col_total_clicks))
THEN 1
ELSE 0
END) AS DOUBLE) / 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,
DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
FROM "your_trino_catalog"."<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
What's next
- Learn how to configure data quality checks in DQOps
- Look at the examples of running data quality checks, targeting tables and columns