Building the Best Synthetic Data Generator in Python for 2026: Why I Am Building Misata and How to...

The article discusses the development of a new synthetic data generator called Misata, designed to address limitations found in existing tools like Faker and SDV. The author outlines why they believe Misata is necessary for generating realistic, internally consistent datasets from intent rather than mirroring real data.

Key Points:

1. Purpose:

   • Misata aims to generate synthetic data that tells a coherent business story without relying on actual data.
   • It addresses the need for realistic test environments and product demos by creating relationally consistent data quickly.

2. Comparison with Existing Tools:

   • Faker: A tool for generating fake data but lacks referential integrity across tables.
   • SDV (Synthetic Data Vault): Mirrors real datasets, useful for privacy-safe copies but not ideal for starting from scratch or creating custom narratives.

3. Core Features of Misata:

   • Referential Integrity: Ensures foreign keys are valid by generating data in the correct dependency order.
   • Multi-Table Generation: Supports relational schemas with guaranteed referential integrity.
   • Domain Coverage: Covers seven predefined domains (Healthcare, Fintech, etc.) and uses LLMs for custom schema inference.

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Read the full article at Towards AI - Medium

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