/datamimic

AI-driven, model-based toolkit designed to transform your data testing workflow

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DATAMIMIC Community Edition

Introduction

Welcome to the official GitHub repository for DATAMIMIC Community Edition! Our platform leverages model and AI-driven technologies to innovate the realm of test data generation, ensuring robust data privacy through sophisticated anonymization and pseudonymization techniques.

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What is DATAMIMIC?

DATAMIMIC is an advanced platform designed to create scalable and reliable test data using AI and model-driven methodologies, ensuring efficiency and compliance with global data protection standards like GDPR. Note: The Community Edition does not include the Rust core, which is part of the Enterprise Edition.

Key Features

  • Model-Based Test Data Generation: Utilize AI to simulate real-world data scenarios for comprehensive testing.
  • Data Anonymization and Pseudonymization: Achieve compliance with stringent data privacy laws.
  • Scalable and Performant: Engineered for high efficiency and scalability to handle complex datasets.

Coming Soon

The community edition of DATAMIMIC will soon be open for public contribution. This collaborative project allows developers and testers globally to contribute and utilize our tools for non-commercial purposes.

How to Contribute

Join us in this open-source journey:

  • Code Contributions: Enhance functionalities or patch bugs through pull requests.
  • Documentation: Expand our docs or help localize them.
  • Community Engagement: Support users and promote widespread adoption.

Installation Instructions

Coming soon...

License

This project is under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), permitting free use, modification, and sharing non-commercially as long as the original creator is credited.

Acknowledgments

A heartfelt thank you to all our contributors for driving DATAMIMIC towards global accessibility.

Stay Tuned!

For regular updates, please star and watch this repository. Learn more about our solutions on Datamimic and Rapiddweller.