Problem Statement

Problem Statement

Real data engineering and application teams need production-like data in lower environments. Production records are often unavailable or inappropriate for development, QA, SIT, UAT, sandboxes, demonstrations, and performance tests because of privacy, security, PII, PHI, PCI, internal policy, or governance constraints.

The work still has to move:

• pipelines need schema and transformation tests
• APIs need contract and integration tests
• dashboards need realistic categories and dates
• data quality systems need clean and intentionally dirty records
• developers need local and shared test fixtures
• platforms need reproducible datasets without production dependencies

Great Generator addresses that gap by creating fake, non-production synthetic data from schema definitions. Engineers provide the expected fields and data types, and receive a Pandas or Spark DataFrame they can validate, transform, or write using familiar tools.

What this does not claim

Great Generator is not a data masking product, an anonymization system, or proof of regulatory compliance. It does not fit a statistical model to sensitive source data. Review generated values and follow your organization's policies.