Synthetic Data Generation

The ydata-sdk empowers data scientists and AI engineers to generate high-fidelity synthetic data across diverse formats—ensuring privacy preservation while maintaining critical statistical integrity. Whether you're working with structured databases or unstructured documents, the SDK offers flexible tools for scalable synthetic data generation.

🔥 New: Synthetic Text Data for LLMs and Foundation Models

ydata-sdk now includes native support for synthetic text data generation, designed to accelerate LLM training, evaluation, and red-teaming use cases. This includes:

• Synthetic Question & Answer (Q&A) Generation – Extract and synthesize Q&A pairs from documents to support LLM benchmarking, supervised fine-tuning, and RAG (Retrieval-Augmented Generation) workflows.

• Synthetic Document Generation – Automatically generate synthetic documents in PDF, DOCX, or HTML format. Ideal for foundational model pretraining or fine-tuning.

These capabilities enable safer and more efficient experimentation with large language models, while solving data availability and compliance challenges.

Core Features for Synthetic Data Generation

• Tabular Synthetic Data – Generate realistic synthetic tables using statistical modeling or generative networks.
• Timeseries Synthetic Data – Simulate temporal patterns and events while maintaining temporal dependencies.
• Synthetic Data with Privacy Levels – Control privacy guarantees such as k-anonymity and differential privacy during generation.
• Synthetic Data with Calculated Features – Automatically preserve business rules, derived fields, and dependencies in your synthetic dataset.
• Faker Synthesizer from Source – Use bootstrapping from real data schemas to generate realistic data with minimal configuration.
• Faker Synthesizer from Scratch – Generate synthetic data based solely on metadata and constraints—no original data needed.

Advanced MultiTable Support

• Multitable Synthetic Data with Calculated Features – Maintain relationships and calculated fields across multiple linked tables.
• MultiTable Synthetic Data with Attribute Tables – Synthesize hierarchical or normalized schemas with dependencies and entity resolution logic.

Click on any section to learn more about its implementation and how to integrate synthetic data into your workflows.