SDV

SDV is a Python library that generates synthetic tabular data by training machine learning models on existing datasets.

The Synthetic Data Vault is a Python library that enables users to train generative AI models on real tabular data. It supports single-table, multi-table, and sequential data formats, allowing for the generation of synthetic data on demand. The library includes built-in tools for evaluating statistical quality and comparing synthetic outputs with real data through visualization. Users can customize synthesizers with specific constraints, preprocessing adjustments, and anonymization options. It functions on-premises using standard CPUs, making it suitable for environments requiring data privacy.

SDV at a glance

Best for: Software engineer, Researcher
Runs on: self-hosted
Pricing: SDV Community is open source; SDV Enterprise is available to licensed users.
Released: May 2018
Category: AI coding tool
Website: docs.sdv.dev

Key features

Train generative AI models for tabular data
Support for single, multi-table, and sequential data types
Evaluate statistical quality and data diagnostics
Visualize comparisons between real and synthetic data
Apply constraints and data preprocessing
Implement data anonymization options

Use it for

Generate synthetic data for complex interconnected tables
Deploy synthetic data applications enterprise-wide
Anonymize sensitive data for analysis
Compare synthetic data quality against real source data

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