Install
Install with a hyphen, then import with an underscore.
pip install great-generator
from great_generator import generate_from_schemaStart here
Getting started with Great Generator
Use this page when you want the shortest path from installation to a useful synthetic dataset.
Great Generator creates synthetic data. It does not anonymize, mask, de-identify, or transform production records.
Generate a DataFrame
Start with a schema string or dictionary. The returned object follows the selected engine: Pandas by default, Spark when requested.
from great_generator import generate_from_schema
schema = "customer_id int, customer_name string, email string, signup_date date"
df = generate_from_schema(schema=schema, rows=1000)
print(df.head())Next steps
Try SQL DDL ingestion, relational generation, Spark and Delta workflows, query-aware generation, or the optional MCP server when an assistant should call Great Generator tools locally.
Related documentation
Keep exploring
Installation optionsInstall Great Generator for core Pandas workflows, Spark, Delta Lake, optional AI advisors, or the optional local MCP server.
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.