Last updated: July 22, 2025

Median change 30 days data quality checks, SQL examples

This check detects that the median of numeric values has changed more than max_percent from the median value measured thirty days ago. This check aims to overcome a monthly seasonability and compare a value to a similar value a month ago.

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The median change 30 days data quality check has the following variants for each type of data quality checks supported by DQOps.

profile median change 30 days

Check description

Verifies that the median in a column changed in a fixed rate since the last readout from the last month.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
profile_median_change_30_days	Maximum relative change in the median of numeric values vs 30 days ago	anomaly	profiling		Consistency	percentile	change_percent_30_days

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the profile median change 30 days data quality check.

Managing profile median change 30 days check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

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 -col=column_name -ch=profile_median_change_30_days --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_* -col=column_name -ch=profile_median_change_30_days --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=profile_median_change_30_days --enable-warning
                    -Wmax_percent=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 -col=column_name -ch=profile_median_change_30_days --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_* -col=column_name -ch=profile_median_change_30_days --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=profile_median_change_30_days --enable-error
                    -Emax_percent=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_median_change_30_days check on all tables and columns on a single data source.

dqo> check run -c=data_source_name -ch=profile_median_change_30_days

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_median_change_30_days

You can also run this check on all tables (and columns) on which the profile_median_change_30_days check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=profile_median_change_30_days

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:
  columns:
    target_column:
      profiling_checks:
        anomaly:
          profile_median_change_30_days:
            parameters:
              percentile_value: 0.5
            warning:
              max_percent: 10.0
              exact_day: false
            error:
              max_percent: 20.0
              exact_day: false
            fatal:
              max_percent: 50.0
              exact_day: false
      labels:
      - This is the column that is analyzed for data quality issues

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 percentile data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        PERCENTILE_CONT(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

SELECT
    quantile({{ parameters.percentile_value }})({{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    ORDER BY {{ lib.render_target_column('original_table')}}
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    quantile(0.5)(analyzed_table."target_column") AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
    ORDER BY original_table."target_column"
) analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM `<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM  AS analyzed_table

HANA

Sensor template for HANARendered SQL for HANA

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    time_period,
    time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
            WITHIN GROUP (ORDER BY {{ lib.render_target_column('original_table')}})
            OVER (
                {%- if lib.data_groupings is not none or lib.time_series is not none %}
                PARTITION BY
                {%- endif -%}
                        {{ render_local_time_dimension_projection('original_table') -}}
                        {{ render_local_data_grouping_projections('original_table') }}
                    ) AS actual_value
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(indentation = '    ', table_alias_prefix='original_table') -}}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
            WITHIN GROUP (ORDER BY original_table."target_column")
            OVER (
                    ) AS actual_value
    FROM "<target_schema>"."<target_table>" original_table) analyzed_table

Oracle

Sensor template for OracleRendered SQL for Oracle

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table

Presto

Sensor template for PrestoRendered SQL for Presto

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

SELECT
    APPROX_PERCENTILE({{ lib.render_target_column('analyzed_table')}} * 1.0, {{ parameters.percentile_value }}, 2) 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
    APPROX_PERCENTILE(analyzed_table."target_column" * 1.0, 0.5, 2) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_table>" original_table
) analyzed_table

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table

Spark

Sensor template for SparkRendered SQL for Spark

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM `<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}CAST({{ lib.render_time_dimension_expression(table_alias_prefix) }} AS DATETIME)
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_time_period_columns() -%}
    {% if lib.time_series is not none -%}
        nested_table.[time_period], nested_table.[time_period_utc]
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.[time_period] AS time_period,
    nested_table.[time_period_utc] AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
        WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{% if lib.time_series is not none or (data_groupings is not none and (data_groupings | length()) > 0) -%}
GROUP BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
ORDER BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
{%- endif -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
        WITHIN GROUP (ORDER BY analyzed_table.[target_column])
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table) AS nested_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}} * 1.0) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column" * 1.0) AS actual_value
FROM "<target_schema>"."<target_table>" analyzed_table

Trino

Sensor template for TrinoRendered SQL for Trino

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_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
  columns:
    target_column:
      profiling_checks:
        anomaly:
          profile_median_change_30_days:
            parameters:
              percentile_value: 0.5
            warning:
              max_percent: 10.0
              exact_day: false
            error:
              max_percent: 20.0
              exact_day: false
            fatal:
              max_percent: 50.0
              exact_day: false
      labels:
      - This is the column that is analyzed for data quality issues
    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 percentile sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM(
    SELECT
        PERCENTILE_CONT(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            analyzed_table.`country`,
            analyzed_table.`state`
        ) AS actual_value
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

SELECT
    quantile({{ parameters.percentile_value }})({{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    ORDER BY {{ lib.render_target_column('original_table')}}
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    quantile(0.5)(analyzed_table."target_column") 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
    ORDER BY original_table."target_column"
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            analyzed_table.`country`,
            analyzed_table.`state`
        ) AS actual_value
    FROM `<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for HANARendered SQL for HANA

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    time_period,
    time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
            WITHIN GROUP (ORDER BY {{ lib.render_target_column('original_table')}})
            OVER (
                {%- if lib.data_groupings is not none or lib.time_series is not none %}
                PARTITION BY
                {%- endif -%}
                        {{ render_local_time_dimension_projection('original_table') -}}
                        {{ render_local_data_grouping_projections('original_table') }}
                    ) AS actual_value
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(indentation = '    ', table_alias_prefix='original_table') -}}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
            WITHIN GROUP (ORDER BY original_table."target_column")
            OVER (
                PARTITION BY
            original_table."country",
            original_table."state"
                    ) AS actual_value
    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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for PrestoRendered SQL for Presto

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            analyzed_table."country",
            analyzed_table."state"
        ) AS actual_value
    FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

SELECT
    APPROX_PERCENTILE({{ lib.render_target_column('analyzed_table')}} * 1.0, {{ parameters.percentile_value }}, 2) 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
    APPROX_PERCENTILE(analyzed_table."target_column" * 1.0, 0.5, 2) 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

Sensor template for RedshiftRendered SQL for Redshift

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for SnowflakeRendered SQL for Snowflake

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for SparkRendered SQL for Spark

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            analyzed_table.`country`,
            analyzed_table.`state`
        ) AS actual_value
    FROM `<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}CAST({{ lib.render_time_dimension_expression(table_alias_prefix) }} AS DATETIME)
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_time_period_columns() -%}
    {% if lib.time_series is not none -%}
        nested_table.[time_period], nested_table.[time_period_utc]
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.[time_period] AS time_period,
    nested_table.[time_period_utc] AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
        WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{% if lib.time_series is not none or (data_groupings is not none and (data_groupings | length()) > 0) -%}
GROUP BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
ORDER BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
{%- endif -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
        WITHIN GROUP (ORDER BY analyzed_table.[target_column])
        OVER (PARTITION BY
            analyzed_table.[country],
            analyzed_table.[state]
        ) AS actual_value
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table) AS nested_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}} * 1.0) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column" * 1.0) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Trino

Sensor template for TrinoRendered SQL for Trino

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            analyzed_table."country",
            analyzed_table."state"
        ) AS actual_value
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

---

daily median change 30 days

Check description

Verifies that the median in a column changed in a fixed rate since the last readout from the last month.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
daily_median_change_30_days	Maximum relative change in the median of numeric values vs 30 days ago	anomaly	monitoring	daily	Consistency	percentile	change_percent_30_days

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the daily median change 30 days data quality check.

Managing daily median change 30 days check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

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 -col=column_name -ch=daily_median_change_30_days --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_* -col=column_name -ch=daily_median_change_30_days --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_median_change_30_days --enable-warning
                    -Wmax_percent=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 -col=column_name -ch=daily_median_change_30_days --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_* -col=column_name -ch=daily_median_change_30_days --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_median_change_30_days --enable-error
                    -Emax_percent=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_median_change_30_days check on all tables and columns on a single data source.

dqo> check run -c=data_source_name -ch=daily_median_change_30_days

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_median_change_30_days

You can also run this check on all tables (and columns) on which the daily_median_change_30_days check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=daily_median_change_30_days

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:
  columns:
    target_column:
      monitoring_checks:
        daily:
          anomaly:
            daily_median_change_30_days:
              parameters:
                percentile_value: 0.5
              warning:
                max_percent: 10.0
                exact_day: false
              error:
                max_percent: 20.0
                exact_day: false
              fatal:
                max_percent: 50.0
                exact_day: false
      labels:
      - This is the column that is analyzed for data quality issues

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 percentile data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        PERCENTILE_CONT(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

SELECT
    quantile({{ parameters.percentile_value }})({{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    ORDER BY {{ lib.render_target_column('original_table')}}
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    quantile(0.5)(analyzed_table."target_column") AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
    ORDER BY original_table."target_column"
) analyzed_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM `<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM  AS analyzed_table

HANA

Sensor template for HANARendered SQL for HANA

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    time_period,
    time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
            WITHIN GROUP (ORDER BY {{ lib.render_target_column('original_table')}})
            OVER (
                {%- if lib.data_groupings is not none or lib.time_series is not none %}
                PARTITION BY
                {%- endif -%}
                        {{ render_local_time_dimension_projection('original_table') -}}
                        {{ render_local_data_grouping_projections('original_table') }}
                    ) AS actual_value
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(indentation = '    ', table_alias_prefix='original_table') -}}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
            WITHIN GROUP (ORDER BY original_table."target_column")
            OVER (
                    ) AS actual_value
    FROM "<target_schema>"."<target_table>" original_table) analyzed_table

Oracle

Sensor template for OracleRendered SQL for Oracle

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table

Presto

Sensor template for PrestoRendered SQL for Presto

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

SELECT
    APPROX_PERCENTILE({{ lib.render_target_column('analyzed_table')}} * 1.0, {{ parameters.percentile_value }}, 2) 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
    APPROX_PERCENTILE(analyzed_table."target_column" * 1.0, 0.5, 2) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_table>" original_table
) analyzed_table

Redshift

Sensor template for RedshiftRendered SQL for Redshift

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table

Spark

Sensor template for SparkRendered SQL for Spark

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM `<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}CAST({{ lib.render_time_dimension_expression(table_alias_prefix) }} AS DATETIME)
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_time_period_columns() -%}
    {% if lib.time_series is not none -%}
        nested_table.[time_period], nested_table.[time_period_utc]
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.[time_period] AS time_period,
    nested_table.[time_period_utc] AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
        WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{% if lib.time_series is not none or (data_groupings is not none and (data_groupings | length()) > 0) -%}
GROUP BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
ORDER BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
{%- endif -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
        WITHIN GROUP (ORDER BY analyzed_table.[target_column])
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table) AS nested_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}} * 1.0) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column" * 1.0) AS actual_value
FROM "<target_schema>"."<target_table>" analyzed_table

Trino

Sensor template for TrinoRendered SQL for Trino

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            NULL
        ) AS actual_value
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_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
  columns:
    target_column:
      monitoring_checks:
        daily:
          anomaly:
            daily_median_change_30_days:
              parameters:
                percentile_value: 0.5
              warning:
                max_percent: 10.0
                exact_day: false
              error:
                max_percent: 20.0
                exact_day: false
              fatal:
                max_percent: 50.0
                exact_day: false
      labels:
      - This is the column that is analyzed for data quality issues
    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 percentile sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM(
    SELECT
        PERCENTILE_CONT(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            analyzed_table.`country`,
            analyzed_table.`state`
        ) AS actual_value
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

SELECT
    quantile({{ parameters.percentile_value }})({{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    ORDER BY {{ lib.render_target_column('original_table')}}
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    quantile(0.5)(analyzed_table."target_column") 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
    ORDER BY original_table."target_column"
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            analyzed_table.`country`,
            analyzed_table.`state`
        ) AS actual_value
    FROM `<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for HANARendered SQL for HANA

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    time_period,
    time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
            WITHIN GROUP (ORDER BY {{ lib.render_target_column('original_table')}})
            OVER (
                {%- if lib.data_groupings is not none or lib.time_series is not none %}
                PARTITION BY
                {%- endif -%}
                        {{ render_local_time_dimension_projection('original_table') -}}
                        {{ render_local_data_grouping_projections('original_table') }}
                    ) AS actual_value
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(indentation = '    ', table_alias_prefix='original_table') -}}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
            WITHIN GROUP (ORDER BY original_table."target_column")
            OVER (
                PARTITION BY
            original_table."country",
            original_table."state"
                    ) AS actual_value
    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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for PrestoRendered SQL for Presto

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            analyzed_table."country",
            analyzed_table."state"
        ) AS actual_value
    FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

SELECT
    APPROX_PERCENTILE({{ lib.render_target_column('analyzed_table')}} * 1.0, {{ parameters.percentile_value }}, 2) 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
    APPROX_PERCENTILE(analyzed_table."target_column" * 1.0, 0.5, 2) 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

Sensor template for RedshiftRendered SQL for Redshift

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for SnowflakeRendered SQL for Snowflake

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for SparkRendered SQL for Spark

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            analyzed_table.`country`,
            analyzed_table.`state`
        ) AS actual_value
    FROM `<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}CAST({{ lib.render_time_dimension_expression(table_alias_prefix) }} AS DATETIME)
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_time_period_columns() -%}
    {% if lib.time_series is not none -%}
        nested_table.[time_period], nested_table.[time_period_utc]
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.[time_period] AS time_period,
    nested_table.[time_period_utc] AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
        WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{% if lib.time_series is not none or (data_groupings is not none and (data_groupings | length()) > 0) -%}
GROUP BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
ORDER BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
{%- endif -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
        WITHIN GROUP (ORDER BY analyzed_table.[target_column])
        OVER (PARTITION BY
            analyzed_table.[country],
            analyzed_table.[state]
        ) AS actual_value
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table) AS nested_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}} * 1.0) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column" * 1.0) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Trino

Sensor template for TrinoRendered SQL for Trino

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            analyzed_table."country",
            analyzed_table."state"
        ) AS actual_value
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

---

daily partition median change 30 days

Check description

Verifies that the median in a column changed in a fixed rate since the last readout from the last month.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
daily_partition_median_change_30_days	Maximum relative change in the median of numeric values vs 30 days ago	anomaly	partitioned	daily	Consistency	percentile	change_percent_30_days

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the daily partition median change 30 days data quality check.

Managing daily partition median change 30 days check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

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 -col=column_name -ch=daily_partition_median_change_30_days --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_* -col=column_name -ch=daily_partition_median_change_30_days --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_partition_median_change_30_days --enable-warning
                    -Wmax_percent=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 -col=column_name -ch=daily_partition_median_change_30_days --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_* -col=column_name -ch=daily_partition_median_change_30_days --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_partition_median_change_30_days --enable-error
                    -Emax_percent=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_median_change_30_days check on all tables and columns on a single data source.

dqo> check run -c=data_source_name -ch=daily_partition_median_change_30_days

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_median_change_30_days

You can also run this check on all tables (and columns) on which the daily_partition_median_change_30_days check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=daily_partition_median_change_30_days

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
  columns:
    target_column:
      partitioned_checks:
        daily:
          anomaly:
            daily_partition_median_change_30_days:
              parameters:
                percentile_value: 0.5
              warning:
                max_percent: 10.0
                exact_day: false
              error:
                max_percent: 20.0
                exact_day: false
              fatal:
                max_percent: 50.0
                exact_day: false
      labels:
      - This is the column that is analyzed for data quality issues
    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 percentile data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
FROM(
    SELECT
        PERCENTILE_CONT(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            CAST(analyzed_table.`date_column` AS DATE),
            TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE))
        ) 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) AS nested_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

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

SELECT
    quantile({{ parameters.percentile_value }})({{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    ORDER BY {{ lib.render_target_column('original_table')}}
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    quantile(0.5)(analyzed_table."target_column") AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS DATE) AS time_period,
    toDateTime64(CAST(original_table."date_column" AS DATE), 3) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
    ORDER BY original_table."target_column"
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Databricks

Sensor template for DatabricksRendered SQL for Databricks

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            CAST(analyzed_table.`date_column` AS DATE),
            TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE))
        ) 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) AS nested_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for DuckDBRendered SQL for DuckDB

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for HANARendered SQL for HANA

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    time_period,
    time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
            WITHIN GROUP (ORDER BY {{ lib.render_target_column('original_table')}})
            OVER (
                {%- if lib.data_groupings is not none or lib.time_series is not none %}
                PARTITION BY
                {%- endif -%}
                        {{ render_local_time_dimension_projection('original_table') -}}
                        {{ render_local_data_grouping_projections('original_table') }}
                    ) AS actual_value
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(indentation = '    ', table_alias_prefix='original_table') -}}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
            WITHIN GROUP (ORDER BY original_table."target_column")
            OVER (
                PARTITION BY
            CAST(original_table."date_column" AS DATE),
             CAST(original_table."date_column" AS DATE)
                    ) AS actual_value,
    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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for PrestoRendered SQL for Presto

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            CAST(analyzed_table."date_column" AS date),
             CAST(analyzed_table."date_column" AS date)
        ) 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 "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

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

SELECT
    APPROX_PERCENTILE({{ lib.render_target_column('analyzed_table')}} * 1.0, {{ parameters.percentile_value }}, 2) 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
    APPROX_PERCENTILE(analyzed_table."target_column" * 1.0, 0.5, 2) 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

Sensor template for RedshiftRendered SQL for Redshift

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for SnowflakeRendered SQL for Snowflake

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for SparkRendered SQL for Spark

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            CAST(analyzed_table.`date_column` AS DATE),
            TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE))
        ) 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) AS nested_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}CAST({{ lib.render_time_dimension_expression(table_alias_prefix) }} AS DATETIME)
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_time_period_columns() -%}
    {% if lib.time_series is not none -%}
        nested_table.[time_period], nested_table.[time_period_utc]
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.[time_period] AS time_period,
    nested_table.[time_period_utc] AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
        WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{% if lib.time_series is not none or (data_groupings is not none and (data_groupings | length()) > 0) -%}
GROUP BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
ORDER BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
{%- endif -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table.[time_period] AS time_period,
    nested_table.[time_period_utc] AS time_period_utc
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
        WITHIN GROUP (ORDER BY analyzed_table.[target_column])
        OVER (PARTITION BY
            CAST(analyzed_table.[date_column] AS date),
            CAST(CAST(analyzed_table.[date_column] AS date) AS DATETIME)
        ) 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) AS nested_table
GROUP BY nested_table.[time_period], nested_table.[time_period_utc]
ORDER BY nested_table.[time_period], nested_table.[time_period_utc]

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}} * 1.0) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column" * 1.0) 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>" analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Trino

Sensor template for TrinoRendered SQL for Trino

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            CAST(analyzed_table."date_column" AS date),
             CAST(analyzed_table."date_column" AS date)
        ) 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 "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_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
  columns:
    target_column:
      partitioned_checks:
        daily:
          anomaly:
            daily_partition_median_change_30_days:
              parameters:
                percentile_value: 0.5
              warning:
                max_percent: 10.0
                exact_day: false
              error:
                max_percent: 20.0
                exact_day: false
              fatal:
                max_percent: 50.0
                exact_day: false
      labels:
      - This is the column that is analyzed for data quality issues
    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 percentile sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM(
    SELECT
        PERCENTILE_CONT(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            CAST(analyzed_table.`date_column` AS DATE),
            TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)),
            analyzed_table.`country`,
            analyzed_table.`state`
        ) 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) AS nested_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

Sensor template for ClickHouseRendered SQL for ClickHouse

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

SELECT
    quantile({{ parameters.percentile_value }})({{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    ORDER BY {{ lib.render_target_column('original_table')}}
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    quantile(0.5)(analyzed_table."target_column") 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,
    toDateTime64(CAST(original_table."date_column" AS DATE), 3) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
    ORDER BY original_table."target_column"
) 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

Sensor template for DatabricksRendered SQL for Databricks

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            CAST(analyzed_table.`date_column` AS DATE),
            TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)),
            analyzed_table.`country`,
            analyzed_table.`state`
        ) 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) AS nested_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

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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,
    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

Sensor template for DuckDBRendered SQL for DuckDB

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for HANARendered SQL for HANA

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    time_period,
    time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
            WITHIN GROUP (ORDER BY {{ lib.render_target_column('original_table')}})
            OVER (
                {%- if lib.data_groupings is not none or lib.time_series is not none %}
                PARTITION BY
                {%- endif -%}
                        {{ render_local_time_dimension_projection('original_table') -}}
                        {{ render_local_data_grouping_projections('original_table') }}
                    ) AS actual_value
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
    {{- lib.render_where_clause(indentation = '    ', table_alias_prefix='original_table') -}}
) analyzed_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(analyzed_table.actual_value) AS actual_value,
    time_period,
    time_period_utc,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
            WITHIN GROUP (ORDER BY original_table."target_column")
            OVER (
                PARTITION BY
            CAST(original_table."date_column" AS DATE),
             CAST(original_table."date_column" AS DATE),
            original_table."country",
            original_table."state"
                    ) AS actual_value,
    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

Oracle

Sensor template for OracleRendered SQL for Oracle

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    TRUNC(CAST(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

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for PrestoRendered SQL for Presto

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            CAST(analyzed_table."date_column" AS date),
             CAST(analyzed_table."date_column" AS date),
            analyzed_table."country",
            analyzed_table."state"
        ) 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 "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_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

Sensor template for QuestDBRendered SQL for QuestDB

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

SELECT
    APPROX_PERCENTILE({{ lib.render_target_column('analyzed_table')}} * 1.0, {{ parameters.percentile_value }}, 2) 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
    APPROX_PERCENTILE(analyzed_table."target_column" * 1.0, 0.5, 2) 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

Sensor template for RedshiftRendered SQL for Redshift

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for SnowflakeRendered SQL for Snowflake

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}}) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column") 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

Sensor template for SparkRendered SQL for Spark

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}TIMESTAMP({{ lib.render_time_dimension_expression(table_alias_prefix) }})
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table.`time_period` AS time_period,
    nested_table.`time_period_utc` AS time_period_utc,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM(
    SELECT
        PERCENTILE(
        (analyzed_table.`target_column`),
        0.5)
        OVER (PARTITION BY
            CAST(analyzed_table.`date_column` AS DATE),
            TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)),
            analyzed_table.`country`,
            analyzed_table.`state`
        ) 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) AS nested_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }}CAST({{ lib.render_time_dimension_expression(table_alias_prefix) }} AS DATETIME)
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_time_period_columns() -%}
    {% if lib.time_series is not none -%}
        nested_table.[time_period], nested_table.[time_period_utc]
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table.[time_period] AS time_period,
    nested_table.[time_period_utc] AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT({{ parameters.percentile_value }})
        WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{% if lib.time_series is not none or (data_groupings is not none and (data_groupings | length()) > 0) -%}
GROUP BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
ORDER BY {{render_time_period_columns()}} {{- lib.render_data_grouping_projections('analyzed_table', set_leading_comma=(lib.time_series is not none)) }}
{%- endif -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table.[time_period] AS time_period,
    nested_table.[time_period_utc] AS time_period_utc,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2
FROM(
    SELECT
        PERCENTILE_CONT(0.5)
        WITHIN GROUP (ORDER BY analyzed_table.[target_column])
        OVER (PARTITION BY
            CAST(analyzed_table.[date_column] AS date),
            CAST(CAST(analyzed_table.[date_column] AS date) AS DATETIME),
            analyzed_table.[country],
            analyzed_table.[state]
        ) 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) AS nested_table
GROUP BY nested_table.[time_period], nested_table.[time_period_utc],
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2
ORDER BY nested_table.[time_period], nested_table.[time_period_utc],
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2

Teradata

Sensor template for TeradataRendered SQL for Teradata

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

SELECT
    PERCENTILE_CONT({{ parameters.percentile_value }})
    WITHIN GROUP (ORDER BY {{ lib.render_target_column('analyzed_table')}} * 1.0) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    PERCENTILE_CONT(0.5)
    WITHIN GROUP (ORDER BY analyzed_table."target_column" * 1.0) 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>" 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

Sensor template for TrinoRendered SQL for Trino

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.time_series is not none -%}
        {{- lib.eol() -}}
        {{ indentation }}{{ lib.render_time_dimension_expression(table_alias_prefix) }},{{ lib.eol() -}}
        {{ indentation }} {{ lib.render_time_dimension_expression(table_alias_prefix) }}
        {{- "," if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '            ') -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ lib.eol() }}{{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ lib.eol() }}{{ indentation }}{{ table_alias_prefix }}.{{ lib.quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
                {{ "," if not loop.last }}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
    MAX(nested_table.actual_value) AS actual_value {{-"," if lib.time_series is not none -}}
    {% if lib.time_series is not none %}
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc
    {%- endif -%}
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST({{ lib.render_target_column('analyzed_table')}} AS DOUBLE),
            {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {%- if lib.data_groupings is none and lib.time_series is none %}
            NULL
            {%- endif -%}
            {{render_local_time_dimension_projection('analyzed_table') -}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    MAX(nested_table.actual_value) AS actual_value,
    nested_table."time_period" AS time_period,
    nested_table."time_period_utc" AS time_period_utc,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM(
    SELECT
        APPROX_PERCENTILE(
            CAST(analyzed_table."target_column" AS DOUBLE),
            0.5)
        OVER (PARTITION BY
            CAST(analyzed_table."date_column" AS date),
             CAST(analyzed_table."date_column" AS date),
            analyzed_table."country",
            analyzed_table."state"
        ) 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 "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table) AS nested_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