Notebook-friendly
Schema strings and dictionaries are useful for quick Pandas examples in notebooks.
df = generate_from_schema("id int, name string, email string", rows=100)Examples
Examples and demo workflows
Use these examples as starting points for demos, notebooks, lower-environment tests, and documentation walkthroughs.
Great Generator creates synthetic data. It does not anonymize, mask, de-identify, or transform production records.
Spark-friendly
Spark DataFrames can be written by the user's runtime to configured storage or connectors.
spark_df.write.mode("overwrite").parquet("/tmp/great_generator/customers")Assistant-friendly
The optional MCP server lets compatible assistants call selected local tools and return file paths plus previews.
Related documentation
Keep exploring
Getting started with Great GeneratorInstall Great Generator and create synthetic non-production data from schemas, SQL DDL, and related-table definitions for development and testing.
Generate synthetic data from schemasCreate synthetic test data from schema strings, dictionaries, Pandas DataFrames, and Spark schemas using semantic field detection.
Spark, Databricks, Delta, and lakehouse workflowsUse Great Generator in Spark and Databricks notebooks to create synthetic datasets and write to Parquet, Delta, DBFS, ADLS, S3, GCS, or configured connectors.
Optional local MCP serverInstall the optional Great Generator MCP server so compatible AI assistants can call schema generation, DDL parsing, relational generation, coverage validation, and local export tools.
GitHub repositorySource code, issues, releases, and project history.
PyPI packageInstall Great Generator from Python Package Index.