SDV

Why we included this project

SDV learns the statistical patterns in your real tabular data and produces synthetic copies you can use without exposing actual records. It is not limited to a single table: it can also model connected relational databases and sequential or time-series records, so you can match the approach to how your data is actually structured. The library spans classical statistical methods such as GaussianCopula through deep-learning GAN models, which gives you a practical trade-off between generation speed and fidelity. It also ships evaluation tooling that compares generated rows against the source and produces a quality report, so you can judge whether the output is trustworthy before building on it. That generation-plus-validation pairing is what makes it useful for data engineers standing up test environments, ML teams that need training data without privacy risk, and analysts who want safe versions of datasets to share externally.