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Multi-Table Synthetic data generation

Multi-Table or Database's synthetic data generation is a powerful method to create high-quality artificial datasets that mirror the statistical properties and relational structures of original multi-table databases. A multi-table database consists of multiple interrelated tables, often with various data types (dates, categorical, numerical, etc.) and complex relationships between records. Key use cases include privacy-preserving access to full production databases and the creation of realistic test environments. Synthetic data allows organizations to share and analyze full production databases without exposing sensitive information, ensuring compliance with data privacy regulations. Additionally, it is invaluable for creating realistic test environments, enabling developers and testers to simulate real-world scenarios, identify potential issues, and validate database applications without risking data breaches. By leveraging synthetic multi-table data, organizations can simulate complex relational data environments, enhance the robustness of database applications, and ensure data privacy, making it a valuable tool for industries that rely on intricate data structures and interdependencies.

Tutorials & Recipes

To get-started with Synthetic Data Generation you can follow out quickstart guide.

For more tutorial and recipes, follow the link to YData's Academy.