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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.

TableDailyPartitionedCheckCategoriesSpec

Container of table level daily partitioned checks. Contains categories of daily partitioned checks.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
volume Volume daily partitioned data quality checks that verify the quality of every day of data separately TableVolumeDailyPartitionedChecksSpec
timeliness Daily partitioned timeliness checks TableTimelinessDailyPartitionedChecksSpec
custom_sql Custom SQL daily partitioned data quality checks that verify the quality of every day of data separately TableCustomSqlDailyPartitionedChecksSpec
uniqueness Daily partitioned uniqueness checks on a table level. TableUniquenessDailyPartitionChecksSpec
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. TableComparisonDailyPartitionedChecksSpecMap
custom Dictionary of custom checks. The keys are check names within this category. CustomCheckSpecMap

TableVolumeDailyPartitionedChecksSpec

Container of table level date partitioned volume data quality checks.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
daily_partition_row_count Verifies that each daily partition in 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 partition is not empty. TableRowCountCheckSpec
daily_partition_row_count_anomaly Detects outstanding partitions whose volume (the row count) differs too much from the average daily partition size. It uses time series anomaly detection to find the outliers in the partition volume during the last 90 days. TableRowCountAnomalyStationaryPartitionCheckSpec
daily_partition_row_count_change Detects when the partition's volume (row count) change between the current daily partition and the previous partition exceeds the maximum accepted change percentage. TableRowCountChangeCheckSpec
daily_partition_row_count_change_1_day Detects when the partition volume change (increase or decrease of the row count) since yesterday's daily partition exceeds the maximum accepted change percentage. TableRowCountChange1DayCheckSpec
daily_partition_row_count_change_7_days This check verifies that the percentage of change in the partition'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_partition_row_count_change_30_days This check verifies that the percentage of change in the partition'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

TableRowCountAnomalyStationaryPartitionCheckSpec

This check detects anomalies in the day-to-day changes to the table volume (the row count). It captures the row count for each day and compares the row count change (increase or decrease) since the previous day. This check raises a data quality issue when the change is in the top anomaly_percent percentage of the biggest day-to-day changes.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
parameters Data quality check parameters TableVolumeRowCountSensorParametersSpec
warning Alerting threshold that raises a data quality warning that is considered as a passed data quality check AnomalyPartitionRowCountRuleWarning1PctParametersSpec
error Default alerting threshold for a set number of rows with negative value in a column that raises a data quality alert AnomalyPartitionRowCountRuleError05PctParametersSpec
fatal Alerting threshold that raises a fatal data quality issue which indicates a serious data quality problem AnomalyPartitionRowCountRuleFatal01PctParametersSpec
schedule_override Run check scheduling configuration. Specifies the schedule (a cron expression) when the data quality checks are executed by the scheduler. CronScheduleSpec
comments Comments for change tracking. Please put comments in this collection because YAML comments may be removed when the YAML file is modified by the tool (serialization and deserialization will remove non tracked comments). CommentsListSpec
disabled Disables the data quality check. Only enabled data quality checks and monitorings are executed. The check should be disabled if it should not work, but the configuration of the sensor and rules should be preserved in the configuration. boolean
exclude_from_kpi Data quality check results (alerts) are included in the data quality KPI calculation by default. Set this field to true in order to exclude this data quality check from the data quality KPI calculation. boolean
include_in_sla Marks the data quality check as part of a data quality SLA (Data Contract). The data quality SLA is a set of critical data quality checks that must always pass and are considered as a Data Contract for the dataset. boolean
quality_dimension Configures a custom data quality dimension name that is different than the built-in dimensions (Timeliness, Validity, etc.). string
display_name Data quality check display name that can be assigned to the check, otherwise the check_display_name stored in the parquet result files is the check_name. string
data_grouping Data grouping configuration name that should be applied to this data quality check. The data grouping is used to group the check's result by a GROUP BY clause in SQL, evaluating the data quality check for each group of rows. Use the name of one of data grouping configurations defined on the parent table. string
always_collect_error_samples Forces collecting error samples for this check whenever it fails, even if it is a monitoring check that is run by a scheduler, and running an additional query to collect error samples will impose additional load on the data source. boolean
do_not_schedule Disables running this check by a DQOps CRON scheduler. When a check is disabled from scheduling, it can be only triggered from the user interface or by submitting "run checks" job. boolean

AnomalyPartitionRowCountRuleWarning1PctParametersSpec

Data quality rule that detects anomalies on the row count of daily partitions. The rule identifies the top X% of anomalous values, based on the distribution of the changes using a standard deviation. The rule uses the time window of the last 90 days, but at least 30 historical measures must be present to run the calculation.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
anomaly_percent The probability (in percent) that the current daily row count is an anomaly because the value is outside the regular range of previous partition volume measures. The default time window of 90 time periods (days, etc.) is used, but at least 30 readouts must exist to run the calculation. double
use_ai Use an AI model to predict anomalies. WARNING: anomaly detection by AI models is not supported in a trial distribution of DQOps. Please contact DQOps support to upgrade your instance to a full DQOps instance. boolean

AnomalyPartitionRowCountRuleFatal01PctParametersSpec

Data quality rule that detects anomalies on the row count of daily partitions. The rule identifies the top X% of anomalous values, based on the distribution of the changes using a standard deviation. The rule uses the time window of the last 90 days, but at least 30 historical measures must be present to run the calculation.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
anomaly_percent The probability (in percent) that the current daily row count is an anomaly because the value is outside the regular range of previous partition volume measures. The default time window of 90 time periods (days, etc.) is used, but at least 30 readouts must exist to run the calculation. double
use_ai Use an AI model to predict anomalies. WARNING: anomaly detection by AI models is not supported in a trial distribution of DQOps. Please contact DQOps support to upgrade your instance to a full DQOps instance. boolean

TableTimelinessDailyPartitionedChecksSpec

Container of table level date partitioned timeliness data quality checks.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
daily_partition_data_ingestion_delay Daily partitioned check calculating the time difference in days between the most recent event timestamp and the most recent ingestion timestamp TableDataIngestionDelayCheckSpec
daily_partition_reload_lag Daily partitioned check calculating the longest time a row waited to be loaded, it is the maximum difference in days between the ingestion timestamp and the event timestamp column on any row in the monitored partition TablePartitionReloadLagCheckSpec
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

TableCustomSqlDailyPartitionedChecksSpec

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_partition_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 a separate data quality check result for each daily partition. TableSqlConditionFailedCheckSpec
daily_partition_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 a separate data quality check result for each daily partition. TableSqlConditionPassedPercentCheckSpec
daily_partition_sql_aggregate_expression_on_table Verifies that a custom aggregated SQL expression (MIN, MAX, etc.) is not outside the expected range. Stores a separate data quality check result for each daily partition. TableSqlAggregateExpressionCheckSpec
daily_partition_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

TableUniquenessDailyPartitionChecksSpec

Container of table level daily partition 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_partition_duplicate_record_count Verifies that the number of duplicate record values in a table does not exceed the maximum accepted count. TableDuplicateRecordCountCheckSpec
daily_partition_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

TableComparisonDailyPartitionedChecksSpecMap

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 partitioned 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, TableComparisonDailyPartitionedChecksSpec]

TableComparisonDailyPartitionedChecksSpec

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 partitioned comparison checks.

The structure of this object is described below

 Property name   Description                       Data type   Enum values   Default value   Sample values 
daily_partition_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 on the time period (the daily partition) and all other data grouping columns. Stores the most recent captured value for each daily partition that was analyzed. TableComparisonRowCountMatchCheckSpec
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