datamimic-ce 1.1.1

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DATAMIMIC Community Edition 🌟

Introduction

Welcome to DATAMIMIC, the Model-Driven and AI-powered platform that revolutionizes test data generation! By leveraging advanced AI and model-driven technologies, DATAMIMIC enables developers and testers to create realistic, scalable, and privacy-compliant test data with ease.

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DATAMIMIC Feature Overview 🎯

Core Features 🔵

🧠 Model-Driven Generation

• Create sophisticated data models for consistent test data generation
• Define complex relationships between entities
• Support for nested and hierarchical data structures

📊 Data Types & Integration

• Basic Data Types Support
  • All standard primitive types
  • Complex data structures
  • Custom data type definitions
• Core Database Integration
  • RDBMS support (PostgreSQL, MySQL, Oracle)
  • MongoDB integration
  • Basic import/export functionality

🛡️ Data Privacy & Compliance

• GDPR-compliant data anonymization
• Basic pseudonymization capabilities
• Data masking for sensitive information
• Configurable privacy rules

⚡ Core Capabilities

• High Performance Engine
  • Optimized for large datasets
  • Parallel processing support
  • Memory-efficient operations
• Python Integration
  • Native Python API
  • Seamless dependency management
  • Python script extensions
• Basic Extensibility
  • Custom generator support
  • Plugin architecture
  • Basic scripting capabilities

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Enterprise Features 🟣

🧠 AI-Powered Generation

Advanced AI capabilities for realistic data creation

• GAN-based Synthesis
  • Realistic data patterns
  • Learning from existing datasets
  • Pattern replication
• LLM Integration
  • Natural language content
  • Context-aware generation
  • Semantic consistency
• Automatic Modeling
  • Schema inference
  • Pattern detection
  • Model optimization

🔗 Advanced Integrations

Enterprise-grade system integration capabilities

• Streaming Support
  • Kafka integration
  • Real-time data generation
  • Stream processing
• Enterprise Formats
  • EDI processing
  • Advanced XSD handling
  • Custom format support
• Advanced Connectors
  • Enterprise systems
  • Cloud platforms
  • Legacy systems

🛡️ Enhanced Privacy Features

Advanced data protection and compliance

• Advanced Anonymization
  • Context-aware masking
  • Reversible anonymization
  • Custom privacy rules
• Compliance Tools
  • Audit logging
  • Compliance reporting
  • Policy enforcement
• Enterprise Security
  • Role-based access
  • Encryption support
  • Security audit trails

📈 Advanced Data Validation

Comprehensive data quality assurance

• Validation Framework
  • Custom rule engines
  • Complex validation logic
  • Cross-field validation

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Why Use DATAMIMIC?

Traditional test data generation can be time-consuming and may compromise data privacy. DATAMIMIC addresses these challenges by:

• Reducing Time-to-Market: Quickly generate test data without manual intervention.
• Enhancing Test Coverage: Simulate diverse data scenarios for comprehensive testing.
• Ensuring Compliance: Maintain data privacy and comply with legal regulations.
• Improving Data Quality: Generate realistic data that mirrors production environments.

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Installation

Prerequisites

• Operating System: Windows, macOS, or Linux
• Python: Version 3.10 or higher
• Optional: uv Package Manager for development setup GitHub

User Installation

The simplest way to get started with DATAMIMIC is through pip:

pip install datamimic-ce

Verify the installation:

datamimic --version

Developer Installation

For contributors and developers who want to work with the source code:

1. Install uv Package Manager:

   pip install uv
   

2. Clone and set up the repository:

   git clone https://github.com/rapiddweller/datamimic.git
   cd datamimic
   uv sync
   

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Usage Guide

Basic Usage

1. Create a new data generation project:

   datamimic init my-project
   cd my-project
   

2. Configure your data model in datamimic.xml:

   <setup>
       <generate name="datamimic_user_list" count="100" target="CSV,JSON">
           <variable name="person" entity="Person(min_age=18, max_age=90, female_quota=0.5)"/>
           <key name="id" generator="IncrementGenerator"/>
           <key name="first_name" script="person.given_name"/>
           <key name="last_name" script="person.family_name"/>
           <key name="gender" script="person.gender"/>
           <key name="birth_date" script="person.birthdate" converter="DateFormat('%d.%m.%Y')"/>
           <key name="email" script="person.family_name + '@' + person.given_name + '.de'"/>
           <key name="ce_user" values="True, False"/>
           <key name="ee_user" values="True, False"/>
           <key name="datamimic_lover" constant="DEFINITELY"/>
       </generate>
   </setup>
   

3. Generate data:

   datamimic run datamimic.xml
   

4. Access the generated data in the output directory.

   json export:

   [
   {"id": 1, "first_name": "Mary", "last_name": "Mcgowan", "gender": "female", "birth_date": "1946-05-15T00:00:00", "email": "[e-mail]", "ce_user": false, "ee_user": true, "datamimic_lover": "DEFINITELY"},
   {"id": 2, "first_name": "Gabrielle", "last_name": "Malone", "gender": "female", "birth_date": "1989-11-27T00:00:00", "email": "[e-mail]", "ce_user": false, "ee_user": true, "datamimic_lover": "DEFINITELY"},
   {"id": 4, "first_name": "Margaret", "last_name": "Torres", "gender": "female", "birth_date": "2006-07-13T00:00:00", "email": "[e-mail]", "ce_user": false, "ee_user": false, "datamimic_lover": "DEFINITELY"},
   {"id": 5, "first_name": "Monica", "last_name": "Meyers", "gender": "female", "birth_date": "1983-07-22T00:00:00", "email": "[e-mail]", "ce_user": true, "ee_user": false, "datamimic_lover": "DEFINITELY"},
   {"id": 6, "first_name": "Jason", "last_name": "Davis", "gender": "male", "birth_date": "1941-07-05T00:00:00", "email": "[e-mail]", "ce_user": true, "ee_user": false, "datamimic_lover": "DEFINITELY"},
   {"...":  "..."},
   {"id": 100, "first_name": "Jared", "last_name": "Rivas", "gender": "male", "birth_date": "1975-03-16T00:00:00", "email": "[e-mail]", "ce_user": true, "ee_user": true, "datamimic_lover": "DEFINITELY"}
   ]
   

   csv export:

   id|first_name|last_name|gender|birth_date|email|ce_user|ee_user|datamimic_lover
   1|Mary|Mcgowan|female|1946-05-15 00:00:00|[e-mail]|False|True|DEFINITELY
   2|Gabrielle|Malone|female|1989-11-27 00:00:00|[e-mail]|False|True|DEFINITELY
   3|Antonio|Davis|male|2005-05-12 00:00:00|[e-mail]|False|True|DEFINITELY
   4|Margaret|Torres|female|2006-07-13 00:00:00|[e-mail]|False|False|DEFINITELY
   5|Monica|Meyers|female|1983-07-22 00:00:00|[e-mail]|True|False|DEFINITELY
   ...
   100|Jason|Davis|male|1941-07-05 00:00:00|[e-mail]|True|False|DEFINITELY
   

Advanced Features

DATAMIMIC supports various advanced features:

• Custom Generators: Create your own data generators
• Data Relationships: Define complex relationships between entities
• Import/Export Formats: Support for JSON, XML, CSV, RDBMS and MongoDB
• Import/Export Formats ( only EE ): Kafka, EDI, XSD and more
• Data Anonymization: Anonymize data to comply with privacy regulations
• Data Validation: Define and enforce data validation rules
• Scripting: Extend functionality using Python scripts
• Database Integration: Connect to databases for seamless data generation
• Model-Driven Generation: Utilize models to generate realistic data
• Validation Rules: Define and enforce data validation rules
• Scripting: Extend functionality using Python scripts

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Examples and Demos

Explore our demo collection:

# List available demos
datamimic demo list

# Run a specific demo
datamimic demo create demo-model
datamimic run ./demo-model/datamimic.xml

Key demos include:

• Basic entity generation
• Complex relationships
• Database integration
• Custom generator creation
• Privacy compliance examples

Find more examples in the datamimic_ce/demos directory.

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Contributing

We ❤️ contributions! Here's how you can help:

• Code Contributions: Submit pull requests for new features or bug fixes.
• Documentation: Improve existing docs or help with translations.
• Community Engagement: Join discussions and support other users.

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📜 DATAMIMIC Licensing Options

🌟 Community Edition

Open Source Freedom for Everyone

✨ Key Benefits

• 🔓 MIT License: Maximum freedom for innovation
• 💼 Commercial Ready: Use freely in commercial projects
• 🔄 Modification Rights: Full source code access and modification rights
• 🌍 No Restrictions: Deploy anywhere, anytime

🎁 What's Included

• 📦 Core Features

  • Model-driven data generation
  • Basic data types & integrations
  • GDPR compliance tools

• 👥 Community Support

  • Active GitHub community
  • Public issue tracking
  • Community discussions
  • Regular updates

💫 Perfect For

• Individual developers
• Startups & small teams
• Open source projects
• Learning & evaluation
• POC development

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⭐ Enterprise Edition

Professional Power for Business Success

🚀 Premium Benefits

• 📋 Commercial License: Enterprise-grade flexibility
• 🔐 Advanced Features: Full suite of professional tools
• 🎯 Priority Support: Direct access to expert team
• 🛠️ Custom Solutions: Tailored to your needs

💎 Premium Features

• 🤖 AI Capabilities

  • GAN-based synthesis
  • LLM integration
  • Automated modeling

• 🔗 Enterprise Integration

  • Advanced connectors
  • Kafka streaming
  • EDI support

• 🛡️ Enhanced Security

  • Advanced privacy features
  • Compliance reporting
  • Audit trails

🎯 Ideal For

• Large enterprises
• Financial institutions
• Healthcare organizations
• Government agencies
• High-compliance industries

📞 Get Started

Ready to unlock the full potential of DATAMIMIC?

Contact Our Team:

• 📧 Email: [e-mail]
• 🌐 Visit: datamimic.io/enterprise

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🤝 Compare Editions

Feature	Community	Enterprise
Base Features	✅	✅
Source Code Access	✅	✅
Commercial Use	✅	✅
AI Features	❌	✅
Priority Support	❌	✅
Enterprise Integrations	❌	✅
SLA Support	❌	✅
Custom Development	❌	✅

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"Empower your data generation journey with the right DATAMIMIC edition for your needs"

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Support

Need help or have questions? We're here for you!

• 📚 Documentation
• 💬 GitHub Discussions
• 🐛 Issue Tracker
• 📧 Email Support

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Connect with Us

Stay updated and connect with our community!

• 🌐 Website: www.datamimic.io
• 🏢 Rapiddweller: www.rapiddweller.com
• 💼 LinkedIn: rapiddweller

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Acknowledgments

A big thank you to all our contributors! Your efforts make DATAMIMIC possible.

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Don't forget to ⭐ star and 👀 watch this repository to stay updated!

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Legal Notices

For detailed licensing information, please see the LICENSE file.