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Last updated: July 22, 2025

DQOps YAML file definitions

The definition of YAML files used by DQOps to configure the data sources, monitored tables, and the configuration of activated data quality checks.

TableDailyMonitoringCheckCategoriesSpec

Container of table level daily monitoring. Contains categories of daily monitoring.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
volume Daily monitoring volume data quality checks TableVolumeDailyMonitoringChecksSpec
timeliness Daily monitoring timeliness checks TableTimelinessDailyMonitoringChecksSpec
accuracy Daily monitoring accuracy checks TableAccuracyDailyMonitoringChecksSpec
custom_sql Daily monitoring custom SQL checks TableCustomSqlDailyMonitoringChecksSpec
availability Daily monitoring table availability checks TableAvailabilityDailyMonitoringChecksSpec
schema Daily monitoring table schema checks TableSchemaDailyMonitoringChecksSpec
uniqueness Daily monitoring uniqueness checks on a table level. TableUniquenessDailyMonitoringChecksSpec
comparisons Dictionary of configuration of checks for table comparisons. The key that identifies each comparison must match the name of a data comparison that is configured on the parent table. TableComparisonDailyMonitoringChecksSpecMap
custom Dictionary of custom checks. The keys are check names within this category. CustomCheckSpecMap

TableVolumeDailyMonitoringChecksSpec

Container of table level daily monitoring for volume data quality checks.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
daily_row_count Verifies that the tested table has at least a minimum accepted number of rows. The default configuration of the warning, error and fatal severity rules verifies a minimum row count of one row, which ensures that the table is not empty. Stores the most recent captured row count value for each day when the row count was evaluated. TableRowCountCheckSpec
daily_row_count_anomaly Detects when the row count has changed too much since the previous day. It uses time series anomaly detection to find the biggest volume changes during the last 90 days. TableRowCountAnomalyDifferencingCheckSpec
daily_row_count_change Detects when the volume's (row count) change since the last known row count exceeds the maximum accepted change percentage. TableRowCountChangeCheckSpec
daily_row_count_change_1_day Detects when the volume's change (increase or decrease of the row count) since the previous day exceeds the maximum accepted change percentage. TableRowCountChange1DayCheckSpec
daily_row_count_change_7_days This check verifies that the percentage of change in the table's volume (row count) since seven days ago is below the maximum accepted percentage. Verifying a volume change since a value a week ago overcomes the effect of weekly seasonability. TableRowCountChange7DaysCheckSpec
daily_row_count_change_30_days This check verifies that the percentage of change in the table's volume (row count) since thirty days ago is below the maximum accepted percentage. Comparing the current row count to a value 30 days ago overcomes the effect of monthly seasonability. TableRowCountChange30DaysCheckSpec
custom_checks Dictionary of additional custom checks within this category. The keys are check names defined in the definition section. The sensor parameters and rules should match the type of the configured sensor and rule for the custom check. CustomCategoryCheckSpecMap

TableTimelinessDailyMonitoringChecksSpec

Container of table level daily monitoring for timeliness data quality checks.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
daily_data_freshness Daily calculating the number of days since the most recent event timestamp (freshness) TableDataFreshnessCheckSpec
daily_data_freshness_anomaly Verifies that the number of days since the most recent event timestamp (freshness) changes in a rate within a percentile boundary during the last 90 days. TableDataFreshnessAnomalyCheckSpec
daily_data_staleness Daily calculating the time difference in days between the current date and the most recent data ingestion timestamp (staleness) TableDataStalenessCheckSpec
daily_data_ingestion_delay Daily calculating the time difference in days between the most recent event timestamp and the most recent ingestion timestamp TableDataIngestionDelayCheckSpec
custom_checks Dictionary of additional custom checks within this category. The keys are check names defined in the definition section. The sensor parameters and rules should match the type of the configured sensor and rule for the custom check. CustomCategoryCheckSpecMap

TableAccuracyDailyMonitoringChecksSpec

Container of built-in preconfigured data quality checks on a table level that are verifying the accuracy of the table, comparing it with another reference table.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
daily_total_row_count_match_percent Verifies the total ow count of a tested table and compares it to a row count of a reference table. Stores the most recent captured value for each day when the data quality check was evaluated. TableAccuracyTotalRowCountMatchPercentCheckSpec
custom_checks Dictionary of additional custom checks within this category. The keys are check names defined in the definition section. The sensor parameters and rules should match the type of the configured sensor and rule for the custom check. CustomCategoryCheckSpecMap

TableCustomSqlDailyMonitoringChecksSpec

Container of built-in preconfigured data quality checks on a table level that are using custom SQL expressions (conditions).

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
daily_sql_condition_failed_on_table Verifies that a custom SQL expression is met for each row. Counts the number of rows where the expression is not satisfied, and raises an issue if too many failures were detected. This check is used also to compare values between columns: `{alias}.col_price > {alias}.col_tax`. Stores the most recent count of failed rows for each day when the data quality check was evaluated. TableSqlConditionFailedCheckSpec
daily_sql_condition_passed_percent_on_table Verifies that a minimum percentage of rows passed a custom SQL condition (expression). Reference the current table by using tokens, for example: `{alias}.col_price > {alias}.col_tax`. Stores the most recent captured percentage for each day when the data quality check was evaluated. TableSqlConditionPassedPercentCheckSpec
daily_sql_aggregate_expression_on_table Verifies that a custom aggregated SQL expression (MIN, MAX, etc.) is not outside the expected range. Stores the most recent captured value for each day when the data quality check was evaluated. TableSqlAggregateExpressionCheckSpec
daily_sql_invalid_record_count_on_table Runs a custom query that retrieves invalid records found in a table and returns the number of them, and raises an issue if too many failures were detected. This check is used for setting testing queries or ready queries used by users in their own systems (legacy SQL queries). For example, when this check is applied on a age column, the condition can find invalid records in which the age is lower than 18 using an SQL query: `SELECT age FROM {table} WHERE age < 18`. TableSqlInvalidRecordCountCheckSpec
daily_import_custom_result_on_table Runs a custom query that retrieves a result of a data quality check performed in the data engineering, whose result (the severity level) is pulled from a separate table. TableSqlImportCustomResultCheckSpec
custom_checks Dictionary of additional custom checks within this category. The keys are check names defined in the definition section. The sensor parameters and rules should match the type of the configured sensor and rule for the custom check. CustomCategoryCheckSpecMap

TableAvailabilityDailyMonitoringChecksSpec

Container of built-in preconfigured data quality checks on a table level that are detecting the table availability.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
daily_table_availability Verifies availability of a table in a monitored database using a simple query. Stores the most recent table availability status for each day when the data quality check was evaluated. TableAvailabilityCheckSpec
custom_checks Dictionary of additional custom checks within this category. The keys are check names defined in the definition section. The sensor parameters and rules should match the type of the configured sensor and rule for the custom check. CustomCategoryCheckSpecMap

TableSchemaDailyMonitoringChecksSpec

Container of built-in preconfigured volume data quality checks on a table level that are executed as a daily monitoring (checkpoint) checks.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
daily_column_count Detects if the number of column matches an expected number. Retrieves the metadata of the monitored table, counts the number of columns and compares it to an expected value (an expected number of columns). Stores the most recent column count for each day when the data quality check was evaluated. TableSchemaColumnCountCheckSpec
daily_column_count_changed Detects if the count of columns has changed since the most recent day. Retrieves the metadata of the monitored table, counts the number of columns and compares it the last known column count that was captured when this data quality check was executed the last time. Stores the most recent column count for each day when the data quality check was evaluated. TableSchemaColumnCountChangedCheckSpec
daily_column_list_changed Detects if new columns were added or existing columns were removed since the most recent day. Retrieves the metadata of the monitored table and calculates an unordered hash of the column names. Compares the current hash to the previously known hash to detect any changes to the list of columns. TableSchemaColumnListChangedCheckSpec
daily_column_list_or_order_changed Detects if new columns were added, existing columns were removed or the columns were reordered since the most recent day. Retrieves the metadata of the monitored table and calculates an ordered hash of the column names. Compares the current hash to the previously known hash to detect any changes to the list of columns or their order. TableSchemaColumnListOrOrderChangedCheckSpec
daily_column_types_changed Detects if new columns were added, removed or their data types have changed since the most recent day. Retrieves the metadata of the monitored table and calculates an unordered hash of the column names and the data types (including the length, scale, precision, nullability). Compares the current hash to the previously known hash to detect any changes to the list of columns or their types. TableSchemaColumnTypesChangedCheckSpec
custom_checks Dictionary of additional custom checks within this category. The keys are check names defined in the definition section. The sensor parameters and rules should match the type of the configured sensor and rule for the custom check. CustomCategoryCheckSpecMap

TableUniquenessDailyMonitoringChecksSpec

Container of table level daily monitoring for uniqueness data quality checks.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
daily_duplicate_record_count Verifies that the number of duplicate record values in a table does not exceed the maximum accepted count. TableDuplicateRecordCountCheckSpec
daily_duplicate_record_percent Verifies that the percentage of duplicate record values in a table does not exceed the maximum accepted percentage. TableDuplicateRecordPercentCheckSpec
custom_checks Dictionary of additional custom checks within this category. The keys are check names defined in the definition section. The sensor parameters and rules should match the type of the configured sensor and rule for the custom check. CustomCategoryCheckSpecMap

TableComparisonDailyMonitoringChecksSpecMap

Container of comparison checks for each defined data comparison. The name of the key in this dictionary must match a name of a table comparison that is defined on the parent table. Contains the daily monitoring comparison checks for each configured reference table.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
self Dict[string, TableComparisonDailyMonitoringChecksSpec]

TableComparisonDailyMonitoringChecksSpec

Container of built-in comparison (accuracy) checks on a table level that are using a defined comparison to identify the reference table and the data grouping configuration. Contains the daily monitoring comparison checks.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
daily_row_count_match Verifies that the row count of the tested (parent) table matches the row count of the reference table. Compares each group of data with a GROUP BY clause. Stores the most recent captured value for each day when the data quality check was evaluated. TableComparisonRowCountMatchCheckSpec
daily_column_count_match Verifies that the column count of the tested (parent) table matches the column count of the reference table. Only one comparison result is returned, without data grouping. Stores the most recent captured value for each day when the data quality check was evaluated. TableComparisonColumnCountMatchCheckSpec
custom_checks Dictionary of additional custom checks within this category. The keys are check names defined in the definition section. The sensor parameters and rules should match the type of the configured sensor and rule for the custom check. CustomCategoryCheckSpecMap