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median anomaly stationary

median anomaly stationary checks

Description
Column level check that ensures that the median in a monitored column is within a two-tailed percentile from measurements made during the last 90 days. Use in partitioned checks.

profile median anomaly stationary

Check description
Verifies that the median in a column changes in a rate within a percentile boundary during last 90 days.

Check name Check type Time scale Sensor definition Quality rule
profile_median_anomaly_stationary profiling percentile anomaly_stationary_percentile_moving_average

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

dqo> check enable -c=connection_name -ch=profile_median_anomaly_stationary
Run check (Shell)
To run this check provide check name in check run command 
dqo> check run -ch=profile_median_anomaly_stationary
It is also possible to run this check on a specific connection. In order to do this, add the connection name to the below 
dqo> check run -c=connection_name -ch=profile_median_anomaly_stationary
It is additionally feasible to run this check on a specific table. In order to do this, add the table name to the below 
dqo> check run -c=connection_name -t=table_name -ch=profile_median_anomaly_stationary
It is furthermore viable to combine run this check on a specific column. In order to do this, add the column name to the below 
dqo> check run -c=connection_name -t=table_name -col=column_name -ch=profile_median_anomaly_stationary
Check structure (Yaml) 
      profiling_checks:
        anomaly:
          profile_median_anomaly_stationary:
            parameters:
              percentile_value: 0.5
            warning:
              anomaly_percent: 0.1
            error:
              anomaly_percent: 0.1
            fatal:
              anomaly_percent: 0.1
Sample configuration (Yaml)
# yaml-language-server: $schema=https://cloud.dqo.ai/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  columns:
    target_column:
      profiling_checks:
        anomaly:
          profile_median_anomaly_stationary:
            parameters:
              percentile_value: 0.5
            warning:
              anomaly_percent: 0.1
            error:
              anomaly_percent: 0.1
            fatal:
              anomaly_percent: 0.1
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

BigQuery

{% 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) }})
    {%- 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 -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    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

    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH),
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH))

        ) AS actual_value,
    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH)) AS time_period_utc
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

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

SELECT
    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
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- 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(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

{% 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,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

{% 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,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

{% 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,
    DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if 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)
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if data_groupings is not none and (data_groupings | length()) > 0 -%}
        {%- for attribute in data_groupings -%}
            {%- with data_grouping_level = 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 }}.{{ quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- 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
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
GROUP BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}
ORDER BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}

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

    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0),
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME)

        ) AS actual_value,
    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0) AS time_period,
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME) AS time_period_utc
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table) 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]

Configuration with data grouping

Click to see more

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  columns:
    target_column:
      profiling_checks:
        anomaly:
          profile_median_anomaly_stationary:
            parameters:
              percentile_value: 0.5
            warning:
              anomaly_percent: 0.1
            error:
              anomaly_percent: 0.1
            fatal:
              anomaly_percent: 0.1
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key
BigQuery 

{% 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) }})
    {%- 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 -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    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

    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH),
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH))

    analyzed_table.`country` AS grouping_level_1
    analyzed_table.`state` AS grouping_level_2
        ) AS actual_value,
    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH)) AS time_period_utc
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table) 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

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
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- 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(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

PostgreSQL 

{% 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,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift 

{% 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,
    DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(LOCALTIMESTAMP AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake 

{% 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,
    DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date))) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server 

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if 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)
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if data_groupings is not none and (data_groupings | length()) > 0 -%}
        {%- for attribute in data_groupings -%}
            {%- with data_grouping_level = 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 }}.{{ quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- 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
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
GROUP BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}
ORDER BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}

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

    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0),
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME)

        ) AS actual_value,
    DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0) AS time_period,
    CAST((DATEADD(month, DATEDIFF(month, 0, SYSDATETIMEOFFSET()), 0)) AS DATETIME) AS time_period_utc
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table) 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

daily median anomaly stationary

Check description
Verifies that the median in a column changes in a rate within a percentile boundary during last 90 days.

Check name Check type Time scale Sensor definition Quality rule
daily_median_anomaly_stationary recurring daily percentile anomaly_stationary_percentile_moving_average

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

dqo> check enable -c=connection_name -ch=daily_median_anomaly_stationary
Run check (Shell)
To run this check provide check name in check run command 
dqo> check run -ch=daily_median_anomaly_stationary
It is also possible to run this check on a specific connection. In order to do this, add the connection name to the below 
dqo> check run -c=connection_name -ch=daily_median_anomaly_stationary
It is additionally feasible to run this check on a specific table. In order to do this, add the table name to the below 
dqo> check run -c=connection_name -t=table_name -ch=daily_median_anomaly_stationary
It is furthermore viable to combine run this check on a specific column. In order to do this, add the column name to the below 
dqo> check run -c=connection_name -t=table_name -col=column_name -ch=daily_median_anomaly_stationary
Check structure (Yaml) 
      recurring_checks:
        daily:
          anomaly:
            daily_median_anomaly_stationary:
              parameters:
                percentile_value: 0.5
              warning:
                anomaly_percent: 0.1
              error:
                anomaly_percent: 0.1
              fatal:
                anomaly_percent: 0.1
Sample configuration (Yaml)
# yaml-language-server: $schema=https://cloud.dqo.ai/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  columns:
    target_column:
      recurring_checks:
        daily:
          anomaly:
            daily_median_anomaly_stationary:
              parameters:
                percentile_value: 0.5
              warning:
                anomaly_percent: 0.1
              error:
                anomaly_percent: 0.1
              fatal:
                anomaly_percent: 0.1
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

BigQuery

{% 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) }})
    {%- 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 -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    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(CURRENT_TIMESTAMP() AS DATE),
    TIMESTAMP(CAST(CURRENT_TIMESTAMP() AS DATE))

        ) AS actual_value,
    CAST(CURRENT_TIMESTAMP() AS DATE) AS time_period,
    TIMESTAMP(CAST(CURRENT_TIMESTAMP() AS DATE)) AS time_period_utc
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

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

SELECT
    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
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- 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(CURRENT_TIMESTAMP AS DATE)) AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

{% 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(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

{% 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(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

{% 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(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date) AS time_period,
    TO_TIMESTAMP(CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if 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)
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if data_groupings is not none and (data_groupings | length()) > 0 -%}
        {%- for attribute in data_groupings -%}
            {%- with data_grouping_level = 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 }}.{{ quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- 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
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
GROUP BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}
ORDER BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}

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(SYSDATETIMEOFFSET() AS date),
    CAST((CAST(SYSDATETIMEOFFSET() AS date)) AS DATETIME)

        ) AS actual_value,
    CAST(SYSDATETIMEOFFSET() AS date) AS time_period,
    CAST((CAST(SYSDATETIMEOFFSET() AS date)) AS DATETIME) AS time_period_utc
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table) 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]

Configuration with data grouping

Click to see more

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  columns:
    target_column:
      recurring_checks:
        daily:
          anomaly:
            daily_median_anomaly_stationary:
              parameters:
                percentile_value: 0.5
              warning:
                anomaly_percent: 0.1
              error:
                anomaly_percent: 0.1
              fatal:
                anomaly_percent: 0.1
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key
BigQuery 

{% 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) }})
    {%- 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 -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    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(CURRENT_TIMESTAMP() AS DATE),
    TIMESTAMP(CAST(CURRENT_TIMESTAMP() AS DATE))

    analyzed_table.`country` AS grouping_level_1
    analyzed_table.`state` AS grouping_level_2
        ) AS actual_value,
    CAST(CURRENT_TIMESTAMP() AS DATE) AS time_period,
    TIMESTAMP(CAST(CURRENT_TIMESTAMP() AS DATE)) AS time_period_utc
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table) 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

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
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- 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(CURRENT_TIMESTAMP AS DATE)) AS time_period,
    CAST(TRUNC(CAST(CURRENT_TIMESTAMP AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

PostgreSQL 

{% 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(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift 

{% 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(LOCALTIMESTAMP AS date) AS time_period,
    CAST((CAST(LOCALTIMESTAMP AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake 

{% 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(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date) AS time_period,
    TO_TIMESTAMP(CAST(TO_TIMESTAMP_NTZ(LOCALTIMESTAMP()) AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server 

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if 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)
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if data_groupings is not none and (data_groupings | length()) > 0 -%}
        {%- for attribute in data_groupings -%}
            {%- with data_grouping_level = 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 }}.{{ quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- 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
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
GROUP BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}
ORDER BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}

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(SYSDATETIMEOFFSET() AS date),
    CAST((CAST(SYSDATETIMEOFFSET() AS date)) AS DATETIME)

        ) AS actual_value,
    CAST(SYSDATETIMEOFFSET() AS date) AS time_period,
    CAST((CAST(SYSDATETIMEOFFSET() AS date)) AS DATETIME) AS time_period_utc
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table) 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

daily partition median anomaly stationary

Check description
Verifies that the median in a column is within a percentile from measurements made during the last 90 days.

Check name Check type Time scale Sensor definition Quality rule
daily_partition_median_anomaly_stationary partitioned daily percentile anomaly_stationary_percentile_moving_average

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

dqo> check enable -c=connection_name -ch=daily_partition_median_anomaly_stationary
Run check (Shell)
To run this check provide check name in check run command 
dqo> check run -ch=daily_partition_median_anomaly_stationary
It is also possible to run this check on a specific connection. In order to do this, add the connection name to the below 
dqo> check run -c=connection_name -ch=daily_partition_median_anomaly_stationary
It is additionally feasible to run this check on a specific table. In order to do this, add the table name to the below 
dqo> check run -c=connection_name -t=table_name -ch=daily_partition_median_anomaly_stationary
It is furthermore viable to combine run this check on a specific column. In order to do this, add the column name to the below 
dqo> check run -c=connection_name -t=table_name -col=column_name -ch=daily_partition_median_anomaly_stationary
Check structure (Yaml) 
      partitioned_checks:
        daily:
          anomaly:
            daily_partition_median_anomaly_stationary:
              parameters:
                percentile_value: 0.5
              warning:
                anomaly_percent: 0.1
              error:
                anomaly_percent: 0.1
              fatal:
                anomaly_percent: 0.1
Sample configuration (Yaml)
# yaml-language-server: $schema=https://cloud.dqo.ai/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  columns:
    target_column:
      partitioned_checks:
        daily:
          anomaly:
            daily_partition_median_anomaly_stationary:
              parameters:
                percentile_value: 0.5
              warning:
                anomaly_percent: 0.1
              error:
                anomaly_percent: 0.1
              fatal:
                anomaly_percent: 0.1
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

BigQuery

{% 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) }})
    {%- 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 -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    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.`` AS DATE),
    TIMESTAMP(CAST(analyzed_table.`` AS DATE))

        ) AS actual_value,
    CAST(analyzed_table.`` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`` AS DATE)) AS time_period_utc
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table) AS nested_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

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

SELECT
    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
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- 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."" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(original_table."" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

{% 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."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

{% 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."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

{% 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."" AS date) AS time_period,
    TO_TIMESTAMP(CAST(analyzed_table."" AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if 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)
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if data_groupings is not none and (data_groupings | length()) > 0 -%}
        {%- for attribute in data_groupings -%}
            {%- with data_grouping_level = 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 }}.{{ quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- 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
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
GROUP BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}
ORDER BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}

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.[] AS date),
    CAST((CAST(analyzed_table.[] AS date)) AS DATETIME)

        ) AS actual_value,
    CAST(analyzed_table.[] AS date) AS time_period,
    CAST((CAST(analyzed_table.[] AS date)) AS DATETIME) AS time_period_utc
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table) 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]

Configuration with data grouping

Click to see more

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  columns:
    target_column:
      partitioned_checks:
        daily:
          anomaly:
            daily_partition_median_anomaly_stationary:
              parameters:
                percentile_value: 0.5
              warning:
                anomaly_percent: 0.1
              error:
                anomaly_percent: 0.1
              fatal:
                anomaly_percent: 0.1
      labels:
      - This is the column that is analyzed for data quality issues
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key
BigQuery 

{% 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) }})
    {%- 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 -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- lib.render_data_grouping_projections('analyzed_table') }}
FROM(
    SELECT
        PERCENTILE_CONT(
        ({{ lib.render_target_column('analyzed_table')}}),
        {{ parameters.percentile_value }})
        OVER (PARTITION BY
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    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.`` AS DATE),
    TIMESTAMP(CAST(analyzed_table.`` AS DATE))

    analyzed_table.`country` AS grouping_level_1
    analyzed_table.`state` AS grouping_level_2
        ) AS actual_value,
    CAST(analyzed_table.`` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`` AS DATE)) AS time_period_utc
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table) 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

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
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}
) analyzed_table
{{- 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."" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(original_table."" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

PostgreSQL 

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

SELECT
    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."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift 

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

SELECT
    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."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake 

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

SELECT
    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."" AS date) AS time_period,
    TO_TIMESTAMP(CAST(analyzed_table."" AS date)) AS time_period_utc
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server 

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

{%- macro render_local_time_dimension_projection(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if 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)
    {%- endif -%}
{%- endmacro -%}

{%- macro render_local_data_grouping_projections(table_alias_prefix = 'analyzed_table', indentation = '    ') -%}
    {%- if data_groupings is not none and (data_groupings | length()) > 0 -%}
        {%- for attribute in data_groupings -%}
            {%- with data_grouping_level = 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 }}.{{ quote_identifier(data_grouping_level.column) }}
                {%- endif -%}
            {%- endwith %} AS grouping_{{ attribute }}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

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
    {{- 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
            {{render_local_time_dimension_projection('analyzed_table')}}
            {{render_local_data_grouping_projections('analyzed_table') }}
        ) AS actual_value
        {{- lib.render_time_dimension_projection('analyzed_table') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation = '    ') -}}) AS nested_table
GROUP BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}
ORDER BY nested_table.[time_period], nested_table.[time_period_utc] {{- lib.render_data_grouping_projections('analyzed_table') }}

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.[] AS date),
    CAST((CAST(analyzed_table.[] AS date)) AS DATETIME)

        ) AS actual_value,
    CAST(analyzed_table.[] AS date) AS time_period,
    CAST((CAST(analyzed_table.[] AS date)) AS DATETIME) AS time_period_utc
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table) 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