DATAMIMIC Community Edition

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🧠 AI-powered test data generation platform enabling developers to create realistic, scalable, and privacy-compliant test data. Features model-driven data generation, GDPR compliance, and seamless P…

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DATAMIMIC Community Edition 🌟

Maintainability Coverage License Python


šŸš€ Quick Intro

DATAMIMIC is an AI-powered, model-driven test data generation platform designed to quickly deliver realistic, privacy-compliant synthetic data.

āœ… Model-driven | āœ… AI-ready | āœ… Privacy-focused | āœ… Open Source (MIT)

šŸ“ž Book your Free Strategy Call and Demo to explore the full power of our Enterprise solution!


🟢 Community vs 🟣 Enterprise Editions

FeatureCommunityEnterprise
Core Model-driven Generationāœ…āœ…
Python & XML APIsāœ…āœ…
Basic Anonymizationāœ…āœ…
AI-Enhanced Data GenerationāŒāœ…
Advanced Enterprise IntegrationsāŒāœ…
Priority Support & SLAāŒāœ…

šŸ‘‰ Learn more about Enterprise Edition


šŸ“¦ Installation

Install easily via pip:

pip install datamimic-ce

Verify installation:

datamimic version

⚔ Quick Start

Generate realistic data effortlessly:

Python Example:

from datamimic_ce.domains.common.services import PersonService

person_service = PersonService(dataset="US")
person = person_service.generate()

print(f"Person: {person.name}, Email: {person.email}")

XML Example:

<setup>
  <generate name="user_data" count="10" target="CSV">
    <key name="name" entity="Person().name"/>
    <key name="email" entity="Person().email"/>
  </generate>
</setup>

Run XML via CLI:

datamimic run datamimic.xml

āš™ļø Custom Domain Factory Example

Quickly generate test-specific data using DataMimicTestFactory:

customer.xml:

<setup>
    <generate name="customer" count="10">
        <variable name="person" entity="Person(min_age=21, max_age=67)"/>
        <key name="id" generator="IncrementGenerator"/>
        <key name="first_name" script="person.given_name"/>
        <key name="last_name" script="person.family_name"/>
        <key name="email" script="person.email"/>
        <key name="status" values="'active', 'inactive', 'pending'"/>
    </generate>
</setup>

Python Usage:

from datamimic_ce.factory.datamimic_test_factory import DataMimicTestFactory

customer_factory = DataMimicTestFactory("customer.xml", "customer")
customer = customer_factory.create()

print(customer["id"])  # 1
print(customer["first_name"])  # Jose
print(customer["last_name"])   # Ayers

šŸŽÆ Why DATAMIMIC?

  • šŸš€ Accelerate Development: Instantly create test data.
  • šŸ›”ļø Privacy First: Built-in GDPR compliance.
  • šŸ“Š Realistic Data: Authentic, weighted distributions from various data domains.
  • šŸ”§ High Flexibility: Easily model, standardize, and customize data generation processes.
  • šŸ“„šŸ“¤ Versatile Sources: Extensive import/export capabilities (JSON, XML, CSV, RDBMS, MongoDB, etc.).
  • šŸ—ƒļø Metadata-Driven: Operate seamlessly with an integrated metadata model.

🌐 Documentation & Demos

datamimic demo create healthcare-example
datamimic run ./healthcare-example/datamimic.xml

šŸ“˜ Additional Resources


ā“ FAQ

Q: Is Community Edition suitable for commercial projects?

A: Absolutely! DATAMIMIC CE uses the MIT License.

Q: Why upgrade to Enterprise Edition (EE) instead of using Community Edition (CE)?

A: EE provides a web UI, enterprise support, team collaboration, and advanced features like AI-powered test data generation, workflow automation, and compliance tools.

Q: Can I contribute?

A: Yes! See Contributing Guide.


šŸ› ļø Support & Community


🌟 Stay Connected


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