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How it works?

SDV is designed to learn statistical patterns from your training data and then emulate those patterns when creating synthetic data. The patterns are saved in a synthesizer object. You can think of your synthesizer as a model that contains a condensed representation of your data.

1

Data Integration

Load your data into SDV. You can connect your database or load in data from a local file. Then, create a metadata object that contains a description of your data for SDV to use. More Information →

2

Modeling

Create a synthesizer and use it to learn patterns from your training data. Pick from a variety of synthesizers that incorporate various different ML modeling techniques. More Information →

3

Sampling

Use your synthesizer to generate realistic synthetic data on-demand. Or supply instructions to your synthesizer to create specific types of data. When you're done, export the synthetic data into a different file or database. More Information→

4

Evaluation

Evaluate the synthetic data by comparing it against the real data. Check for basic diagnostic stats, compare the statistical quality, and visualize any differences. More Information→

Tutorials

Use SDV Community to explore synthetic data, and create synthesizers for simple, proof-of-concept datasets.

Synthesize a Table (Gaussian Copula)

A fast, customizable and transparent way to synthesize data.

Synthesize a Table (CTGAN)

A GAN-based approach to creating synthetic data with high fidelity.

Synthesize Sequences (PAR)

A neural network-based approach to creating synthetic data.

Synthesize Multi Tables (HMA)

Synthesize data across multiple, connected tables in a database.

Use Your Own Data

Prepare your own data for the SDV: Load your raw data and write a metadata description

Customizations

Using SDV Community, you can control the full workflow when preprocessing data, anonymizing sensitive values, adding constraints and more.

Custom Processing & Anonymization

Improve your synthesizer by customizing the data processing workflow.

Constraint-Augmented Generation

Input business rules into your synthesizer using constraints. This ensures high-quality, valid synthetic data, 100% of the time.