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Great Generator helps engineers generate realistic synthetic data from schema definitions for lower environments, data pipelines, testing, demos, and analytics projects.

Start here if you have a schema but do not have safe test data.

from great_generator import generate_from_schema

schema = {
    "customer_id": "string",
    "customer_name": "string",
    "age": "int",
    "email": "string",
    "balance": "float",
    "created_at": "datetime",
}

df = generate_from_schema(schema, rows=1000)

What it solves

Teams often cannot copy production records into dev, QA, SIT, UAT, sandbox, demo, or performance environments. Great Generator creates fake, non-production data from your schema so you can test without waiting for a production extract.

Start here

1. Problem Statement
2. Quick Start with generate_from_schema
3. Generate Related Tables
4. Supported Schema Input Types
5. Realistic Mode
6. Custom Rules
7. Writing Data to Files and Databases
8. Data Quality and Edge Cases

Schema-first or domain-first?

• Use generate_from_schema for real project structures and lower-environment testing.
• Use generate_relational when several custom tables need valid key relationships.
• Use generate_domain for ready-made demonstrations, tutorials, and learning datasets.

Important boundary

Great Generator creates synthetic data. It does not anonymize, mask, de-identify, or transform real production data. Follow your organization's data governance, privacy, security, and compliance policies.