Install

Installation options

The base package keeps the default install simple. Extras add Spark, Delta, dbt/YAML ingestion, advisor, or MCP dependencies only when needed.

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

Getting started API reference GitHub

Core package

Use the core install for Pandas, schemas, SQL DDL, relational generation, CDC, anomalies, and local file workflows.

pip install great-generator

Spark and Delta extras

Use these extras in PySpark, Databricks, or lakehouse notebooks when the environment supports Spark and Delta dependencies.

pip install "great-generator[spark]"
pip install "great-generator[delta]"

Optional advisor and MCP extras

Install advisor extras only when you want model-assisted planning. Install MCP only when an MCP-compatible assistant should call Great Generator tools locally.

pip install "great-generator[dbt]"
pip install "great-generator[schema-ingest]"
pip install "great-generator[ai]"
pip install "great-generator[mcp]"

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. 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 AI advisor layerUse optional advisor workflows to review schema meaning, generation plans, column tags, and realism reports before deterministic synthetic data generation. 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.