Single Table Synthesizers

The SDV offers a variety of synthesizers, which use different algorithms to generate synthetic data. This section describes single table synthesizers, which act on single table data.

Basic Single Table Synthesizers

These synthesizers are available in SDV Community. They build a generative AI model using your training data, and use it to create synthetic data.

We recommend starting with GaussianCopulaSynthesizer for fast performance, good quality, and customization.

For higher fidelity, try a neural network-based synthesizer such as CTGANSynthesizer or TVAESynthesizer. Modeling and sampling performance may be slower for these synthesizers, especially if you have categorical columns with many different values (high cardinality).

GaussianCopulaSynthesizer

Use a classical ML algorithm to learn from real data. This is fast, transparent, and customizable.

CTGANSynthesizer

Use GAN-based ML algorithm to learn from real data. This may take longer to learn and be harder to debug.

TVAE Synthesizer

Use a variational autoencoder ML model to learn from real data. This may take longer to learn and be harder to debug.

Experimental synthesizer: The CopulaGANSynthesizer combines classical statistics with GAN-based modeling.

Specialty Synthesizers

Specialty synthesizers are available for special situations — such as improving speed, enhancing quality, or providing privacy guarantees.

Specialty synthesizers available for licensed, SDV Enterprise users (denoted by ＊) or through purchasing additional bundles (denoted by ❖). For more information, see the feature comparison.

❖ XGCSynthesizer

Use extra features on top of Gaussian Copula for higher quality synthetic data and improved performance.

❖ BootstrapSynthesizer

A synthesizer that is optimized to learn from a smaller number of training rows.

❖ SegmentSynthesizer

Use this synthesizer when your real data is highly segmented, with different patterns for each.

＊DayZSynthesizer

Generate synthetic data from scratch. Use this when you don't have a lot of real data.

❖ DPGCSynthesizer

Use Gaussian Copula while guaranteeing differential privacy.

❖ DPGCFlexSynthesizer

[Experimental] Use and customize Gaussian Copula while guaranteeing differential privacy.