Let’s put synthetic data to work.

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Single Table

Learn a tabular model to synthesize rows in a table

Multi Table

Learn a relational data model to synthesize multiple, related tables

Time Series

Learn a time series model to synthesize new events

Synthetic Data Vault

Protect and Enhance Your Data

Use synthetic data in place of real data for added protection, or use it in addition to your real data as an enhancement.

PROTECT

Create a clone to protect real data

ENHANCE

Add synthetic values to real data

Test Software · Scale Data Science · Create Demos · Augmented ML · Plan Scenarios

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The SDV Ecosystem

Open Source

The SDV package is an overall system for synthetic data models, benchmarks, and metrics. Explore the ecosystem of open source libraries supporting the SDV. Each can be used as standalone packages for particular needs.

Copulas

Models & generates tabular data with classic statistical methods. Uses multivariate copulas.

Github

Docs

CTGAN

Models & generates tabular data with Deep Learning. Offers CTGAN and TVAE models.

Github

DeepEcho

Models & generates time series data with a mix of classic statistical models and Deep Learning.

Github

RDT

Discovers properties & transforms data for data science use. Reverses the transforms to reproduce realistic data.

Github

Docs

Synthetic Data Vault

Generates synthetic data across single table, relational, and time series data. Supports multiple models & evaluations.

Github

Docs

QUICKSTART

Try it out Now!

Quickly discover SDV with just a few lines of code!

from sdv import load_demo, SDV

# Use pre-loaded demo tables
metadata, tables = load_demo(metadata=True)

sdv = SDV()
sdv.fit(metadata, tables)

synthetic_data = sdv.sample()
print(synthetic_data)

Install SDV

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