Query coverage

Query-aware synthetic data

Query-aware generation helps avoid synthetic datasets that look valid but fail to exercise important filters, joins, or partition paths.

Great Generator creates synthetic data. It does not anonymize, mask, de-identify, or transform production records.

Getting started API reference GitHub

Required values

Ask generated data to include specific business values needed by tests or demos.

df = generate_from_schema(
    schema=schema,
    rows=1000,
    required_values={"region": ["SOUTH"], "account_type": ["CHECKING", "SAVINGS"]},
)

Coverage validation

Validate whether generated output includes required values, partition dates, selectivity targets, and join coverage where configured.

Related documentation

Keep exploring

Generate synthetic data from schemasCreate synthetic test data from schema strings, dictionaries, Pandas DataFrames, and Spark schemas using semantic field detection. SQL DDL synthetic data workflowsParse SQL CREATE TABLE statements into schema contracts and generate synthetic data for ANSI, Spark, and Databricks-style table definitions. 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.