Last updated: July 22, 2025
Data quality rules supported by DQOps
The full list of data quality rules supported by DQOps. The source code is found in the $DQO_HOME/rules/ folder in the DQOps distribution.
averages rules
| Rule name | Description |
|---|---|
| between_percent_moving_average_30_days | Data quality rule that verifies if a data quality sensor readout value is not above X percent of the moving average within a time window. |
| between_percent_moving_average_60_days | Data quality rule that verifies if a data quality sensor readout value is not above X percent of the moving average within a time window. |
| between_percent_moving_average_7_days | Data quality rule that verifies if a data quality sensor readout value is not above X percent of the moving average within a time window. |
| percent_moving_average | Data quality rule that verifies if a data quality sensor readout value is not above X percent of the moving average within a time window. |
| within_percent_moving_average_30_days | Data quality rule that verifies if a data quality sensor readout value is not above X percent of the moving average within a time window. |
| within_percent_moving_average_60_days | Data quality rule that verifies if a data quality sensor readout value is not above X percent of the moving average within a time window. |
| within_percent_moving_average_7_days | Data quality rule that verifies if a data quality sensor readout value is not above X percent of the moving average within a time window. |
change rules
| Rule name | Description |
|---|---|
| between_change | Data quality rule that verifies if data quality sensor readout value changed by a value between the provided bounds. |
| between_change_1_day | Data quality rule that verifies if data quality sensor readout value changed by a value between the provided bounds compared to yesterday. |
| between_change_30_days | Data quality rule that verifies if data quality sensor readout value changed by a value between the provided bounds compared to last month. |
| between_change_7_days | Data quality rule that verifies if data quality sensor readout value changed by a value between the provided bounds compared to last week. |
| between_percent_change | Data quality rule that verifies if data quality sensor readout value changed by a percent between the provided bounds. |
| between_percent_change_1_day | Data quality rule that verifies if data quality sensor readout value changed by a percent between the provided bounds compared to yesterday. |
| between_percent_change_30_days | Data quality rule that verifies if data quality sensor readout value changed by a percent between the provided bounds compared to last month. |
| between_percent_change_7_days | Data quality rule that verifies if data quality sensor readout value changed by a percent between the provided bounds compared to last week. |
| change_difference | Data quality rule that verifies if data quality sensor readout value changed by a value within the provided bound. |
| change_difference_1_day | Data quality rule that verifies if data quality sensor readout value changed by a value within the provided bound compared to yesterday. |
| change_difference_30_days | Data quality rule that verifies if data quality sensor readout value changed by a value within the provided bound compared to last month. |
| change_difference_7_days | Data quality rule that verifies if data quality sensor readout value changed by a value within the provided bound compared to last week. |
| change_percent | Data quality rule that verifies if data quality sensor readout value changed by a percent within the provided bound. |
| change_percent_1_day | Data quality rule that verifies if data quality sensor readout value changed by a percent within the provided bound compared to yesterday. |
| change_percent_30_days | Data quality rule that verifies if data quality sensor readout value changed by a percent within the provided bound compared to last month. |
| change_percent_7_days | Data quality rule that verifies if data quality sensor readout value changed by a percent within the provided bound compared to last week. |
comparison rules
| Rule name | Description |
|---|---|
| between_floats | Data quality rule that verifies if a data quality check readout is between from and to values. |
| between_ints | Data quality rule that verifies if a data quality check readout is between begin and end values. |
| between_percent | Data quality rule that verifies if a data quality check percentage readout is between an accepted range of percentages. |
| count_between | Data quality rule that verifies if a data quality check readout is between begin and end values, defined as min_count and max_count. |
| detected_datatype_equals | Data quality rule that verifies that a data quality check readout of a string_datatype_detect (the data type detection) matches an expected data type. The supported values are in the range 1..8, which are: 1 - integers, 2 - floats, 3 - dates, 4 - datetimes, 5 - timestamps, 6 - booleans, 7 - strings, 8 - mixed data types. |
| diff_percent | Data quality rule that verifies if a data quality check readout is less or equal a maximum value. |
| equals | Data quality rule that verifies that a data quality check readout equals a given value. A margin of error may be configured. |
| equals_0 | Data quality rule that verifies that a data quality check readout equals 0. It is used in data quality checks that have an expected value "0". |
| equals_1 | Data quality rule that verifies that a data quality check readout equals 1. It is used in data quality checks that have an expected value "1". |
| equals_integer | Data quality rule that verifies that a data quality check readout equals a given integer value, with an expected value preconfigured as 1. |
| import_severity | A dummy data quality rule that always fails. It is activated on an import_custom_result data quality check that imports data quality results from different data quality libraries directly from logging tables. |
| max | Data quality rule that verifies if a data quality check readsout is less or equal a maximum value. |
| max_count | Data quality rule that verifies if a data quality check (sensor) readout is less or equal a maximum value. |
| max_days | Data quality rule that verifies if a data quality check (sensor) readout is less or equal a maximum value. |
| max_failures | Data quality rule that verifies if the number of executive failures (the sensor returned 0) is below the max_failures. The default maximum failures is 0 failures (the first failure is reported). |
| max_missing | Data quality rule that verifies the results of the data quality checks that count the number of values present in a column, comparing it to a list of expected values. The rule compares the count of expected values (received as expected_value) to the count of values found in the column (as the actual_value). The rule fails when the difference is higher than the expected max_missing, which is the maximum difference between the expected_value (the count of values in the expected_values list) and the actual number of values found in the column that match the list. |
| max_percent | Data quality rule that verifies if a data quality check readout is less or equal a maximum value. |
| max_value | Data quality rule that verifies if a data quality check readout is less or equal a maximum value. |
| min | Data quality rule that verifies if a data quality check readout is greater or equal a minimum value. |
| min_count | Data quality rule that verifies if a data quality check readout is greater or equal a minimum value. |
| min_count_1 | Data quality rule that verifies if a data quality check readout is greater or equal a minimum value. |
| min_percent | Data quality rule that verifies if a data quality check readout is greater or equal a minimum value. |
| min_value | Data quality rule that verifies if a data quality check readout is greater or equal a minimum value. |
| pass | A dummy data quality rule that always passes. |
| value_changed | Data quality rule that verifies if a data quality check (sensor) readout is less or equal a maximum value. |
percentile rules
| Rule name | Description |
|---|---|
| anomaly_differencing_percentile_moving_average | Data quality rule that detects anomalies in time series of data quality measures that are increasing over time, such as the row count is growing. The rule transforms the recent data quality sensor readouts into a differencing stream, converting values to a difference from the previous value. For the following time series of row count values: [100, 105, 110, 116, 126, 122], the differencing stream is [5, 5, 6, 10, -4], which are the row count changes since the previous day. 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. |
| anomaly_differencing_percentile_moving_average_30_days | Data quality rule that detects anomalies in time series of data quality measures that are increasing over time, such as the row count is growing. The rule transforms the recent data quality sensor readouts into a differencing stream, converting values to a difference from the previous value. For the following time series of row count values: [100, 105, 110, 116, 126, 122], the differencing stream is [5, 5, 6, 10, -4], which are the row count changes since the previous day. 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 30 days, but at least 10 historical measures must be present to run the calculation. |
| anomaly_partition_row_count | 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. |
| anomaly_stationary_count_values | Data quality rule that detects anomalies in a stationary time series of counts of values. 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. |
| anomaly_stationary_percent_values | Data quality rule that detects anomalies in a stationary time series of percentage values (in the range 0..100). 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. |
| anomaly_stationary_percentile_moving_average | Data quality rule that detects anomalies in time series of data quality measures that are stationary over time, such as a percentage of null values. Stationary measures stay within a well-known range of values. 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. |
| anomaly_stationary_percentile_moving_average_30_days | Data quality rule that detects anomalies in time series of data quality measures that are stationary over time, such as a percentage of null values. Stationary measures stay within a well-known range of values. 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 30 days, but at least 10 historical measures must be present to run the calculation. |
| anomaly_timeliness_delay | Data quality rule that detects anomalies in data timeliness. 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. |
| change_percentile_moving_30_days | Data quality rule that verifies if a data quality sensor readout value is probable under the estimated normal distribution based on the increments of previous values gathered within a time window. |
| change_percentile_moving_60_days | Data quality rule that verifies if a data quality sensor readout value is probable under the estimated normal distribution based on the increments of previous values gathered within a time window. |
| change_percentile_moving_7_days | Data quality rule that verifies if a data quality sensor readout value is probable under the estimated normal distribution based on the increments of previous values gathered within a time window. |
| percentile_moving_30_days | Data quality rule that verifies if a data quality sensor readout value is probable under the estimated normal distribution based on the previous values gathered within a time window. |
| percentile_moving_60_days | Data quality rule that verifies if a data quality sensor readout value is probable under the estimated normal distribution based on the previous values gathered within a time window. |
| percentile_moving_7_days | Data quality rule that verifies if a data quality sensor readout value is probable under the estimated normal distribution based on the previous values gathered within a time window. |
stdev rules
| Rule name | Description |
|---|---|
| change_multiply_moving_stdev_30_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average of increments on a time window. |
| change_multiply_moving_stdev_60_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average of increments on a time window. |
| change_multiply_moving_stdev_7_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average of increments on a time window. |
| change_multiply_moving_stdev_within_30_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average of increments on a time window. |
| change_multiply_moving_stdev_within_60_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average of increments on a time window. |
| change_multiply_moving_stdev_within_7_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average of increments on a time window. |
| multiply_moving_stdev_30_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average within a time window. |
| multiply_moving_stdev_60_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average within a time window. |
| multiply_moving_stdev_7_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average within a time window. |
| multiply_moving_stdev_within_30_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average within a time window. |
| multiply_moving_stdev_within_60_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average within a time window. |
| multiply_moving_stdev_within_7_days | Data quality rule that verifies if a data quality sensor readout value doesn't excessively deviate from the moving average within a time window. |