Tutorials

This page contains links to some Python notebooks that showcase the functionality in SDV.

Getting started with SDV Community

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.

Program Your Own Constraint

If you have business rules that are not covered by SDV, you can program your own constraint to adhere to them.