🌊 Delta Lake & Apache Iceberg Knowledge Hub

🌐 GitHub Pages

The GitHub Pages for this repository is available at: Delta Lake & Apache Iceberg Knowledge Hub

🎯 Vision Statement

Building the definitive, community-driven knowledge ecosystem for modern data lakehouse technologies. This repository serves as a living, breathing whitepaper that evolves with the data engineering landscape, combining comprehensive technical comparisons, battle-tested code recipes, and AI-powered content curation to empower data engineers worldwide to make informed architectural decisions and implement best practices for Delta Lake and Apache Iceberg.

📁 Repository Content and Structure

This repository is organized into the following sections:

Core Content

Section	Location	Description
Feature Matrix	docs/comparisons/feature-matrix.md	Comprehensive comparison of Delta Lake vs Apache Iceberg
Code Recipes	code-recipes/	Production-ready code examples with validation
Tutorials	docs/tutorials/	Step-by-step guides for common use cases
Architecture	docs/architecture/	Reference architectures and design patterns
Best Practices	docs/best-practices/	Industry-tested patterns and recommendations

Learning Resources

Resource	Location	Description
Getting Started	docs/tutorials/getting-started.md	Quick start guide for beginners
Migration Guide	docs/tutorials/migration-guide.md	Moving from legacy systems
Knowledge Quiz	quiz/	Test your Delta Lake & Iceberg knowledge
Design System	docs/design-system.md	UI/UX guidelines for the project

📚 Quick Links

• 🔍 Feature Comparison Matrix - Detailed side-by-side comparison of Delta Lake vs Apache Iceberg
• 👨‍💻 Code Recipes - Production-ready code examples with validation
• 🧠 Knowledge Quiz - Test your Delta Lake & Iceberg knowledge
• 📖 Tutorials - Step-by-step guides for common use cases
• 🏗️ Architecture Patterns - Reference architectures and design patterns
• 🤝 Contributing Guide - Join our community and contribute
• 📜 Code of Conduct - Our community standards
• 🏆 Community Leaderboard - Top contributors

💡 The "Living Whitepaper" Philosophy

Unlike traditional static documentation, this repository is designed as a living knowledge base that continuously evolves:

• 🤖 Automated Freshness: GitHub Actions workflows automatically detect stale content and create issues to keep documentation current
• ✅ Validated Content: Every code recipe is automatically tested in CI/CD to ensure it works with the latest versions
• 🔗 Link Health: Automated link checking prevents documentation rot
• 📊 Community-Driven: Contributions are gamified with a points system, encouraging diverse perspectives
• 🧠 AI-Enhanced: Machine learning assists in discovering, summarizing, and curating relevant content from across the web
• 🎨 Diagrams as Code: All architecture diagrams use Mermaid.js for version control and easy collaboration

🛠️ Tech Stack

This knowledge hub leverages cutting-edge technologies:

• 📊 Data Formats: Delta Lake, Apache Iceberg
• 💻 Languages: Python, SQL, Scala
• 🔄 Orchestration: GitHub Actions, Python automation scripts
• 📝 Documentation: Markdown, Mermaid.js
• 🧪 Testing: pytest, shell scripts
• 🎨 Code Quality: black, flake8, markdownlint
• 🔍 Content Discovery: BeautifulSoup, feedparser, LLM APIs

🎯 What You'll Find Here

📊 Comprehensive Comparisons

Our feature comparison matrix provides an unbiased, detailed analysis of:

• Time Travel and Version Control
• Schema Evolution Strategies
• Partitioning and Clustering
• Compaction and Optimization
• Concurrency Control Mechanisms
• Query Performance Characteristics
• Ecosystem Integration

💻 Battle-Tested Code Recipes

Every recipe in our code-recipes directory follows a standardized structure:

• Problem Definition: Clear use case description
• Solution: Fully commented, production-ready code
• Dependencies: Reproducible environment specifications
• Validation: Automated tests to verify functionality

🎓 Learning Resources

• Tutorials: Hands-on guides for common scenarios
• Best Practices: Industry-tested patterns and anti-patterns
• Architecture Guides: Reference implementations for various scales

🚀 How to Use This Material

1. Start with the Feature Comparison: Begin by reading the Feature Comparison Matrix for a comprehensive overview of Delta Lake vs Apache Iceberg.

2. Explore the Getting Started Guide: Use the Getting Started Tutorial to set up your first lakehouse.

3. Review Code Recipes: Work through the Code Recipes for hands-on implementation examples.

4. Follow Best Practices: Study the Best Practices for production-ready implementations.

5. Test Your Knowledge: Take the Knowledge Quiz to validate your understanding.

6. Visit the Website: Explore the full content at GitHub Pages.

🚀 Getting Started

For Learners

1. Browse the feature comparison matrix to understand the differences
2. Explore code recipes for your specific use case
3. Follow tutorials for step-by-step implementations

For Contributors

1. Read our Contributing Guide
2. Check open issues for areas needing help
3. Review the Code of Conduct
4. Submit your first pull request!

🛠️ Development & Deployment

Prerequisites

• Ruby: 2.7+ (for Jekyll)
• Python: 3.8+ (for scripts and validation)
• Node.js: 16+ (optional, for additional tooling)
• Git: Latest version

Local Development Setup

1. Clone the repository

   git clone https://github.com/Analytical-Guide/Datalake-Guide.git
   cd Datalake-Guide

2. Install Jekyll and dependencies

   # Install Bundler if not already installed
   gem install bundler
   
   # Install project dependencies
   bundle install

3. Install Python dependencies (for validation scripts)

   pip install -r requirements-dev.txt

4. Start local development server

   # Serve with live reload
   bundle exec jekyll serve --livereload
   
   # Or build and serve
   bundle exec jekyll build && bundle exec jekyll serve

5. Open your browser

   • Navigate to http://localhost:4000/Datalake-Guide/
   • The site will automatically reload when you make changes

Development Workflow

Making Changes

1. Create a feature branch

   git checkout -b feature/your-feature-name

2. Make your changes

   • Edit Markdown files in docs/, code-recipes/, etc.
   • Update styles in assets/css/main.css
   • Modify scripts in scripts/

3. Test your changes

   # Run validation tests
   python scripts/validate_site.py
   
   # Check for broken links
   python scripts/check_internal_links.py
   
   # Build the site
   bundle exec jekyll build

4. Preview changes locally

   bundle exec jekyll serve

Code Quality

• Markdown: Follow the style guide in CONTRIBUTING.md
• CSS: Use the established design system (see Design System Documentation)
• JavaScript: Follow modern ES6+ standards with accessibility in mind
• Python: Use Black for formatting, follow PEP 8

Automated Testing

Run the comprehensive test suite:

# Run all validation tests
python scripts/validate_site.py

# Check internal links
python scripts/check_internal_links.py

# Validate code recipes
find code-recipes -name "validate.sh" -exec bash {} \;

Deployment

GitHub Pages (Automatic)

The site is automatically deployed to GitHub Pages via GitHub Actions:

1. Push to main branch

   git add .
   git commit -m "Your commit message"
   git push origin main

2. GitHub Actions will:

   • Build the Jekyll site
   • Run validation tests
   • Deploy to GitHub Pages
   • Report any failures

Manual Deployment

For manual deployment or custom environments:

# Build for production
JEKYLL_ENV=production bundle exec jekyll build

# Deploy to custom server
rsync -avz _site/ user@server:/path/to/site/

Environment Configuration

Jekyll Configuration

Key settings in _config.yml:

• url: Site URL for absolute links
• baseurl: Subpath for GitHub Pages
• repository: GitHub repository for links
• plugins: Enabled Jekyll plugins

Custom Variables

Available in _config.yml:

• github_url: Full GitHub repository URL
• issues_url: Issues page URL
• discussions_url: Discussions page URL

Troubleshooting

Common Issues

1. Jekyll build fails

   # Clear Jekyll cache
   rm -rf .jekyll-cache _site
   
   # Reinstall dependencies
   bundle install
   
   # Try building again
   bundle exec jekyll build

2. Python scripts fail

   # Ensure Python 3.8+
   python --version
   
   # Install/update dependencies
   pip install -r requirements-dev.txt

3. Links are broken

   # Run link checker
   python scripts/check_internal_links.py
   
   # Fix any reported issues

4. Styling issues

   • Check browser developer tools for CSS errors
   • Ensure design system variables are used correctly
   • Test responsive design across breakpoints

Getting Help

• Issues: Report bugs
• Discussions: Ask questions
• Documentation: Check local development docs

Performance Monitoring

The site includes performance optimizations:

• Font loading: Optimized with font-display: swap
• CSS: Minified and optimized
• Images: Lazy loading support
• JavaScript: Progressive enhancement

Monitor performance using:

• Lighthouse: Browser dev tools
• WebPageTest: External performance testing
• GitHub Actions: Automated performance checks

📈 Repository Stats

📝 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🤝 Community & Support

• Issues: Report bugs or request features
• Discussions: Join community discussions
• Pull Requests: Contribute code or documentation

🙏 Acknowledgments

This knowledge hub is made possible by our amazing community of contributors. Thank you to everyone who has helped make this resource valuable for data engineers worldwide!

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