Generate with anomalies
Use configured anomaly rates to create test data for data quality rules and pipeline hardening.
data = generate_domain(
"ecommerce",
anomalies={"null_rate": 0.02, "duplicate_rate": 0.01, "outlier_rate": 0.005},
)Data quality
Anomaly injection for data quality testing
By default, generated data should be valid. Anomalies are opt-in so teams can test failure paths intentionally.
Great Generator creates synthetic data. It does not anonymize, mask, de-identify, or transform production records.
Useful for
Validation tests, negative testing, data quality demos, ETL error handling, monitoring demos, and QA datasets.
Related documentation
Keep exploring
CDC simulationCreate synthetic change data capture style records for pipeline testing, ingestion validation, ordering checks, and lower-environment CDC demos.
Generate synthetic data from schemasCreate synthetic test data from schema strings, dictionaries, Pandas DataFrames, and Spark schemas using semantic field detection.
Relational synthetic dataGenerate related synthetic tables with primary-key and foreign-key behavior for joins, warehouse models, tests, demos, and analytics examples.
API reference overviewReview the main Great Generator public APIs for schema generation, SQL DDL parsing, relational generation, CDC, anomalies, advisors, and exports.
GitHub repositorySource code, issues, releases, and project history.
PyPI packageInstall Great Generator from Python Package Index.