# sdv-dev/SDV

Synthetic data generation for tabular data

Repository: https://github.com/sdv-dev/SDV
Canonical: https://ross.abutalabs.com/products/sdv
Homepage: https://docs.sdv.dev/sdv
Language: Python
License: NOASSERTION
License Family: other
Topics: synthetic-data, machine-learning, relational-datasets, multi-table, time-series, synthetic-data-generation, sdv, data-generation, generative-adversarial-network, gan, gans, deep-learning, generative-ai, generative-model, generativeai
Last push: 2026-08-26T18:04:04+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 98, longevity 100
- inputs: {"age_days": 3036, "days_push": 7, "days_rel": 12, "gap_med": 13.0, "n_releases_24m": 45}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3549, forks 419 (observed 2026-08-28T04:08:09.495011+00:00)

## What it is
SDV (Synthetic Data Vault) is a Python library for generating synthetic tabular data using machine learning models ranging from GaussianCopula to CTGAN. It supports single-table, multi-table relational, and sequential data, with evaluation, visualization, anonymization, and constraint features.

## Use cases
- generate synthetic tabular data for testing
- create fake relational multi-table datasets
- anonymize sensitive data while preserving patterns
- generate synthetic time series data
- evaluate quality of synthetic datasets
- train a generative model on my database schema

## When to choose
- you need realistic synthetic versions of single, multi-table, or sequential tabular data
- you want on-premises data generation with standard CPUs
- you need built-in quality evaluation, anonymization, and business-rule constraints

## When to avoid
- you need synthetic images, text, or audio rather than tabular data
- you need to scale to very large, complex interconnected table sets beyond community edition capabilities
- you require a non-Python environment

## Facets
- artifact type: library
- maturity: stable
- function: data-generation, machine-learning, deep-learning, data-science, privacy
- domain: machine-learning, data-science, privacy, developer-tools
- platform: python, cross-platform
- tags: synthetic-data, tabular-data, gan, ctgan, gaussian-copula, anonymization, time-series, multi-table, generative-ai

## Member repositories
- sdv-dev/SDV (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:09.495011+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:34:22.566890+00:00, confidence not recorded.
  - readme: https://github.com/sdv-dev/SDV (fetched 2026-08-28T04:08:09.495011+00:00, sha 8e4b65d0089a)
  - homepage: https://docs.sdv.dev/sdv (fetched 2026-08-29T09:28:25.362006+00:00, sha dc24389c3feb)
  - registry_pypi: https://pypi.org/pypi/sdv/json (fetched 2026-08-29T09:28:25.372030+00:00, sha 66a7895988d7)
- Data as of 2026-08-30T08:39:29.467469+00:00.