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Apache Iceberg Code Practice Wiki

Welcome to the Apache Iceberg Code Practice Wiki! This educational resource provides comprehensive guides and additional learning materials to supplement the hands-on coding labs in the main repository.

📚 Wiki Contents

• Getting Started - Complete setup guide and first steps
• Iceberg Fundamentals - Deep dive into Iceberg concepts and architecture
• Lab Guides - Detailed walkthroughs for each lab
• Best Practices - Production-ready patterns and tips
• Troubleshooting - Common issues and solutions
• Learning Path - Recommended order for completing labs
• Multi-Engine Guide - Working with Spark, Trino, and DuckDB
• Streaming & CDC - Real-time data pipelines with Kafka and Debezium

🎯 Educational Philosophy

This repository follows a hands-on, lab-based approach to learning Apache Iceberg:

• Progressive complexity: Labs range from beginner to advanced
• Real-world scenarios: Practical exercises based on actual use cases
• Multi-engine learning: Experience with different query engines
• Vendor independence: Learn concepts that apply across platforms
• Production patterns: Best practices you can apply in production

🚀 Quick Start

1. Set up your environment
2. Start with Lab 0
3. Follow the learning path
4. Explore advanced topics

📊 Progress Tracking

Track your progress by:

• Marking completed labs in your notebook
• Keeping a checklist of finished exercises
• Timing yourself to measure improvement
• Revisiting labs after learning new concepts

🤝 Community

Join our community of learners:

• Share your solutions and insights
• Ask questions in GitHub Issues
• Contribute new labs and exercises
• Help improve existing content

🔗 Resources

• Main Repository
• Apache Iceberg Documentation
• Apache Spark Documentation
• Trino Documentation
• DuckDB Documentation

🎓 Lab Overview

Beginner Labs (0-2)

• Lab 0: Sample Database Setup
• Lab 1: Environment Setup
• Lab 2: Basic Iceberg Operations

Intermediate Labs (3-5)

• Lab 3: Advanced Features
• Lab 4: Spark Optimizations
• Lab 5: Real-World Patterns

Advanced Labs (6-11)

• Lab 6: Performance & UI
• Lab 7: Table Maintenance
• Lab 8: Kafka Integration
• Lab 9: CDC with Debezium
• Lab 10: Spring Boot with Iceberg
• Lab 11: Multi-Engine Lakehouse

💡 Tips for Success

Start with Fundamentals

• Complete Labs 0-2 before moving to advanced topics
• Understand Iceberg's core concepts (metadata, snapshots, manifests)
• Practice basic table operations thoroughly

Use Multiple Engines

• Try the same operations in Spark, Trino, and DuckDB
• Understand engine-specific optimizations
• Learn which engine is best for which use case

Practice Regularly

• Consistency beats intensity - 30 minutes daily is better than 3 hours weekly
• Revisit labs after breaks to reinforce learning
• Try to complete labs without looking at solutions

Learn from Mistakes

• Read error messages carefully
• Understand why your solution didn't work
• Try alternative approaches
• Check the solution notebooks for patterns

🔧 Environment Options

Kubernetes with k3s (Recommended)

• Full-featured environment
• Production-like setup
• Better resource isolation
• Suitable for long-term learning

Docker Compose (Lightweight)

• Quick to set up
• Lower resource requirements
• Good for initial learning
• Easier to troubleshoot

📈 Skill Development

By completing all labs, you will develop skills in:

• Apache Iceberg table operations and management
• Data lakehouse architecture and design
• Multi-engine query optimization
• Streaming data pipelines with Kafka
• Change data capture with Debezium
• Performance tuning and monitoring
• Production-ready data engineering patterns

🆘 Need Help?

• Check the Troubleshooting page
• Review Best Practices
• Open an issue on GitHub
• Start a discussion in GitHub Discussions
• Check the Learning Path for guidance

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Happy learning! 🎓🏔️