🌊 Delta Lake & Apache Iceberg Knowledge Hub

The definitive, community-driven reference for modern data lakehouse engineering — comparing Delta Lake and Apache Iceberg with production-tested recipes, automated freshness tracking, and weekly AI-powered content discovery.

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🌐 Live Knowledge Hub

Explore the full site at analytical-guide.github.io/Datalake-Guide — searchable documentation, tutorials, and curated resources, automatically kept up to date.

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🎯 What Is This?

Choosing between Delta Lake and Apache Iceberg—or deciding how to deploy either in production—is non-trivial. This hub solves that by providing:

Need	What You'll Find
Understand the differences	Feature comparison matrix with 60+ criteria across 10 dimensions
Get hands-on quickly	CI/CD-validated code recipes for common workloads
Move to production	Production readiness guide with advanced compaction, monitoring, and DR patterns
Migrate existing systems	Step-by-step migration guide covering Parquet, Hive, and cross-cloud scenarios
Stay current	Weekly automated discovery of new articles and blog posts from trusted sources

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📁 Repository Structure

Datalake-Guide/
├── docs/
│   ├── comparisons/feature-matrix.md   ← Delta vs Iceberg (60+ features)
│   ├── tutorials/getting-started.md    ← Quickstart for both formats
│   ├── tutorials/migration-guide.md    ← Parquet → Delta/Iceberg migration
│   ├── best-practices/production-readiness.md ← Production checklist
│   ├── architecture/system-overview.md ← Hub automation architecture
│   └── awesome-list.md                 ← Curated resources (auto-updated)
├── code-recipes/examples/              ← Runnable, validated code recipes
├── community/                          ← Contributor stats & processed URLs
├── scripts/                            ← Automation scripts
└── .github/workflows/                  ← 8 automated GitHub Actions

Core Content

Section	Location	Description
Feature Matrix	docs/comparisons/feature-matrix.md	60+ feature comparison across 10 dimensions
Code Recipes	code-recipes/	CI-validated, production-ready examples
Getting Started	docs/tutorials/getting-started.md	Hands-on quickstart for both technologies
Migration Guide	docs/tutorials/migration-guide.md	Parquet, Hive → Delta/Iceberg with validation scripts
Production Readiness	docs/best-practices/production-readiness.md	Advanced patterns for production deployments
Architecture	docs/architecture/system-overview.md	Hub automation and workflow architecture
Awesome List	docs/awesome-list.md	Curated resources, updated weekly by AI aggregator
Knowledge Quiz	quiz.md	Test and track your knowledge

📚 Quick Links

• 🔍 Feature Comparison Matrix — 60+ criteria, benchmarks, and decision framework
• 👨‍💻 Code Recipes — Production-ready examples with CI validation
• 📖 Getting Started — First Delta/Iceberg table in minutes
• 🚀 Migration Guide — Parquet/Hive → modern format
• 🏗️ Production Readiness — Best practices for production
• 🤝 Contributing Guide — Earn points, join the community
• 📜 Code of Conduct — 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 through automation:

Automation	Trigger	What It Does
Code Recipe CI	Every PR	Lints Python, runs validate.sh per recipe
Documentation CI	Every PR	Markdownlint, link checker, Mermaid diagram validation
Stale Content Bot	Weekly (Mon)	Opens issues for docs untouched > 12 months
Resource Aggregator	Weekly (Sun)	Discovers new articles from RSS feeds, commits to awesome list
Leaderboard Update	Daily	Regenerates top-10 contributor table in README
Gamification Engine	PR/Review/Issue	Awards points and updates contributor stats
Quiz Leaderboard	Issue comment	Updates quiz scores in the leaderboard issue

All architecture diagrams use Mermaid.js so every diagram is version-controlled and diffable alongside the content it describes.

🛠️ Tech Stack

Layer	Technologies
Data Formats	Delta Lake 3.x, Apache Iceberg 1.5+
Languages	Python 3.8+, SQL, Scala
Automation	GitHub Actions (8 workflows)
Documentation	Markdown, Mermaid.js, Jekyll
Code Quality	black, flake8, markdownlint, typos
Link Health	lychee link checker
Content Discovery	feedparser, BeautifulSoup, optional LLM APIs

🚀 How to Use This Material

👩‍🎓 For Learners

Step	Goal	Resource
1	Compare technologies	Feature Matrix
2	Set up your environment	Getting Started Tutorial
3	Try runnable examples	Code Recipes
4	Move to production	Production Readiness Guide
5	Migrate existing systems	Migration Guide
6	Test your knowledge	Knowledge Quiz

👩‍💻 For Contributors

1. Read our Contributing Guide — contributions earn points on the leaderboard
2. Check open issues for areas needing help
3. Review the Code of Conduct
4. Submit your first pull request — the gamification engine awards points automatically!

🛠️ 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

🏆 Community Leaderboard

🏆 Top Contributors

Thank you to our amazing community members who make this knowledge hub possible!

Rank	Contributor	Points	PRs	Reviews	Issues
🥇 #1	@Copilot	75	2	0	0
🥈 #2	@moshesham	13	1	0	1

Last updated: 2026-05-06 13:51 UTC

Want to see your name here? Check out our Contributing Guide to get started!

📈 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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