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Synthetic Data Generator

Synthetic Data Generator (SDG) is an open-source Python framework for generating synthetic structured tabular data. It provides statistical synthesis models such as CTGAN, an LLM-based single-table synthesis model, data connectors, and processing support for table formats and values.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Synthetic Data Generator — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use SDG when a code-based framework is appropriate for generating or experimenting with synthetic structured tables, especially when the workflow calls for CSV input, CTGAN, or documented LLM synthesis and feature-inference examples.

Who it is for

  • Python developers building data-analysis or machine-learning workflows.
  • Teams needing synthetic table data for development, testing, debugging or data sharing.
  • Practitioners exploring LLM-based table synthesis or off-table feature inference.

Documented uses

  • Fit a tabular synthesis model to CSV data and sample synthetic rows.
  • Explore LLM-based generation of single-table data.
  • Infer a new table column from existing data and LLM knowledge.
  • Process column formats and null values around synthesis workflows.
Before you choose

Requirements & limits

The repository is public and identifies the project as open source under the Apache-2.0 license. The README documents installation from PyPI, source, or a pre-built Docker image.

  • The repository's quick-start example is Python code using a CSV connector and CTGAN.
  • The project offers distinct statistical and LLM-based synthesis paths; the LLM workflows have linked Colab examples.
  • The README describes plug-in support for models, processing and data connectors, and mentions differential privacy and anonymization methods.
  • The one-epoch CTGAN example is presented as a quick demo, not as a production configuration.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Generate sample rows from a CSV with CTGAN

Follow the repository's documented quick-start example to fit a CTGAN synthesizer on a CSV connector and request 1,000 synthetic rows.

Before you start

  • Python environment with the SDG package installed using a documented method such as pip install sdgx.
  • A CSV dataset, or the demo dataset downloaded by the example's download_demo_data() utility.

Steps

  1. Create a CsvConnector using the CSV file path, or obtain the demo CSV with download_demo_data().

  2. Initialize Synthesizer with CTGANSynthesizerModel(epochs=1) and the CSV connector.

  3. Call synthesizer.fit().

  4. Call synthesizer.sample(1000) and inspect the resulting data.

Expected outcome

The example produces a sampled table of 1,000 synthetic rows.

Guide limitations

  • The README labels one epoch as a quick-demo setting; it does not define production training settings or dataset-specific quality criteria.
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Evidence and transparency

Sources & review

Official repository ↗
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11 October 2026
Original website title
hitsz-ids/synthetic-data-generator · Official homepage title · retained in its source language
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