{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"SDV generates synthetic tabular data by learning patterns from real datasets using machine learning models, then creates new data that preserves statistical properties while anonymizing sensitive columns.","skillfed_tags":["synthetic-data","privacy-preserving","data-generation"],"use_cases":["Generate test datasets for development and QA without exposing real customer or sensitive data","Create shareable datasets for research or collaboration while maintaining privacy compliance","Augment small datasets with synthetic rows to improve machine learning model training","Evaluate data quality and statistical fidelity between real and synthetic versions","Prototype data pipelines and analytics on realistic synthetic data before deploying to production"],"what_it_does":"SDV is a Python library for generating synthetic tabular data that mimics real datasets while protecting sensitive information. It offers multiple machine learning models\u2014from classical statistical methods like GaussianCopula to deep learning approaches like CTGAN\u2014to learn and replicate patterns in your data. The library handles single tables, multiple connected tables, and sequential data, with built-in support for preprocessing, anonymization, and business rule constraints.\n\nThe typical workflow involves loading or preparing your real data with metadata, selecting a synthesizer model, fitting it to learn patterns, then sampling synthetic rows. SDV also provides evaluation tools to measure how well the synthetic data matches the real data's statistical properties and to visualize differences. It depends on a substantial stack including pandas, numpy, copulas, ctgan, deepecho, rdt, and sdmetrics for its core functionality.","worth_installing":"Yes, with conditions. SDV is actively maintained, has no known vulnerabilities, and low install friction. It's a mature tool (Production/Stable status) for a real need\u2014synthetic data generation with privacy controls. However, the BUSL-1.1 license treatment is unclear; verify the license terms match your use case (commercial, internal, or research) before committing to production deployment. If licensing is acceptable, it's a solid choice for tabular synthetic data work."},"id":"sdv","links":{"html":"https://skillfed.io/packages/sdv","md":"https://skillfed.io/packages/sdv.md","pypi":"https://pypi.org/project/sdv/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":"BUSL-1.1","license_treatment":"unclear","name":"sdv","python_support":"supports_current","summary":"Generate synthetic data for single table, multi table and sequential data"},"popularity":{"monthly_downloads":118605,"position":12113,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.38.0"}