SDV and SDGym
SDGym is a benchmarking framework that evaluates and compares synthetic data generation methods, making it a complement to SDV that enables practitioners to assess SDV's performance against alternative approaches.
About SDV
sdv-dev/SDV
Synthetic data generation for tabular data
Supports multiple synthesis architectures including statistical methods (GaussianCopula) and deep learning approaches (CTGAN) for single, multi-table, and sequential datasets. Includes built-in evaluation metrics comparing synthetic to real data across column distributions and correlations, plus constraint enforcement and PII anonymization during generation.
About SDGym
sdv-dev/SDGym
Benchmarking synthetic data generation methods.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work