Tutorials
Last updated
Last updated
This page contains links to some Python notebooks that showcase the functionality in SDV.
Use SDV Community to explore synthetic data, and create synthesizers for simple, proof-of-concept datasets.
Using SDV Community, you can control the full workflow when preprocessing data, anonymizing sensitive values, adding constraints and more.
A fast, customizable and transparent way to synthesize data.
A GAN-based approach to creating synthetic data with high fidelity.
A neural network-based approach to creating synthetic data.
Synthesize data across multiple, connected tables in a database.
Prepare your own data for the SDV: Load your raw data and write a metadata description
Improve your synthesizer by customizing the data processing workflow.
Input business rules into your synthesizer using constraints. This ensures high-quality, valid synthetic data, 100% of the time.
If you have business rules that are not covered by SDV, you can program your own constraint to adhere to them.