SDV is a Python library for generating synthetic tabular data using machine learning algorithms.
The tool addresses the need to create realistic synthetic datasets that preserve patterns from real data while enabling safe data sharing and testing. It works by training machine learning models on real data to learn its statistical properties and distributions, then using those models to generate new synthetic records. The library supports multiple modeling approaches, from classical statistical methods like GaussianCopula to deep learning techniques such as CTGAN, allowing users to choose based on their data characteristics and requirements. It handles single tables, multiple connected tables with relationships, and sequential or time-series data.
Adoption suits teams needing synthetic data for privacy-preserving data sharing, testing, or augmentation without exposing sensitive information. The tool is appropriate for projects where understanding data quality matters, as it includes evaluation and visualization capabilities to compare synthetic output against real data across multiple quality measures. Users can apply preprocessing, anonymization strategies, and business rule constraints to improve synthetic data quality. The library provides a complete workflow from data loading through synthesis to quality assessment.
The project maintains active development with regular updates to its modeling capabilities and documentation. The codebase receives consistent refinement across its core synthesis functionality and evaluation metrics. Community engagement is supported through multiple channels including tutorials, comprehensive documentation, and a dedicated forum for user discussion and support.
