Dirty data

Dirty-data rules intentionally introduce controlled anomalies after clean values are generated. Use them to exercise validation, observability, and data-quality workflows against imperfect datasets.

Supported rules include missing values, numeric outliers, and type- or length-overflow cases where the target column allows them. Configure a rate to control how often a selected rule is applied.