• 🎯 Educational Mission
• 🎓 Why This Repository?
• 🎓 Learning Approach
• 🏗️ Architecture
• 🛠️ Core Stack
• 🎓 Lab Structure
• 💾 Sample Data
• 🚀 Quick Start
• 📋 Requirements
• 🔧 Configuration
• 📚 Documentation
• 🔗 Related Practice Repositories
• 🆘 Vendor Independence
• 🤝 Contributing
• 👥 Community and Learning
• 📄 License

A comprehensive, vendor-independent Apache Beam learning environment designed for developers, data engineers, and analysts who want to master modern data pipeline engineering through hands-on practice.

15 progressive labs with 120+ exercises covering Apache Beam fundamentals, pipeline development, streaming processing, and production deployment. Completely free and open source. Built for learners, by learners.

This educational resource fills the gap between theoretical knowledge and practical skills in Apache Beam and modern data pipeline engineering:

• Learn by Doing: Progressive hands-on labs build real skills
• Vendor Independent: Master concepts that apply across all runners
• Unified Model: Learn the Beam model for batch and streaming
• Production Patterns: Learn deployment, monitoring, and operations
• Multi-Language Experience: Work with Python, Java, and SQL
• Community Driven: Built and improved by the data engineering community

Our labs are designed to build knowledge progressively:

• Beginner (Labs 0-2): Foundation and basic pipeline concepts
• Intermediate (Labs 3-6): Advanced transforms and I/O
• Advanced (Labs 7-10): Streaming, state, and production deployment

Each lab includes:

• Clear Learning Objectives: Know what you'll achieve
• Step-by-Step Instructions: Guided exercises
• Real-World Scenarios: Practical pipeline use cases
• Solution Notebooks: Reference implementations
• Conceptual Guides: Deep-dive explanations

┌─────────────────────────────────────────────────────────────┐
│                  Apache Beam Code Practice                │
│                  Data Pipeline Learning Environment         │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌──────────────────────────────────────────────────────┐  │
│  │         Apache Beam Unified Model                 │  │
│  │         - PCollection abstraction               │  │
│  │         - Transform functions                   │  │
│  │         - Pipeline I/O connectors                │  │
│  │         - Windowing and triggers                 │  │
│  └──────────────────────────────────────────────────────┘  │
│                              ↓                              │
│  ┌──────────────────────────────────────────────────────┐  │
│  │         Pipeline Development                     │  │
│  │         - Batch processing patterns              │  │
│  │         - Streaming processing patterns           │  │
│  │         - State management                       │  │
│  │         - Windowing strategies                   │  │
│  └──────────────────────────────────────────────────────┘  │
│                              ↓                              │
│  ┌──────────────────────────────────────────────────────┐  │
│  │         Execution Runners                        │  │
│  │         - DirectRunner (local)                   │  │
│  │         - Dataflow (cloud)                      │  │
│  │         - Spark (cluster)                        │  │
│  │         - Flink (streaming)                      │  │
│  └──────────────────────────────────────────────────────┘  │
│                              ↓                              │
│  ┌──────────────────────────────────────────────────────┐  │
│  │         Data Sources & Sinks                     │  │
│  │         - File systems (GCS, S3, HDFS)           │  │
│  │         - Pub/Sub, Kafka (streaming)              │  │
│  │         - Databases (BigQuery, Spanner)           │  │
│  │         - Custom I/O connectors                  │  │
│  └──────────────────────────────────────────────────────┘  │
│                              ↓                              │
│  ┌──────────────────────────────────────────────────────┐  │
│  │         Production Operations                   │  │
│  │         - Pipeline deployment                   │  │
│  │         - Monitoring and logging                 │  │
│  │         - Testing and debugging                  │  │
│  │         - Performance optimization               │  │
│  └──────────────────────────────────────────────────────┘  │
│                                                              │
└─────────────────────────────────────────────────────────────┘

• Apache Beam: Unified model for batch and streaming
• Python SDK: Pipeline development with Python
• Java SDK: Pipeline development with Java
• SQL Support: Beam SQL for declarative pipelines

• DirectRunner: Local execution for development
• Dataflow: Managed cloud execution
• Spark: Cluster execution
• Flink: Streaming execution

• File Systems: GCS, S3, HDFS, local files
• Streaming: Pub/Sub, Kafka
• Databases: BigQuery, Spanner, JDBC
• Custom I/O: Extensible connector framework

• Install Apache Beam and dependencies
• Test pipeline execution locally
• Validate runner configurations
• Explore different SDKs

• Understand Apache Beam fundamentals
• Learn the Beam model and concepts
• Explore PCollection and transforms
• Build your first pipeline

• Create and execute basic pipelines
• Understand pipeline I/O
• Practice common transforms
• Work with schema and data types

• ParDo, Map, Filter, GroupByKey
• Combine, Flatten, CoGroupByKey
• Custom transforms and DoFns
• Pipeline optimization basics

• File system I/O (GCS, S3, local)
• Database I/O (BigQuery, JDBC)
• Streaming I/O (Pub/Sub, Kafka)
• Custom I/O connectors

• Fixed and sliding windows
• Session windows
• Trigger strategies
• Late data handling

• Stateful processing
• Timers and user state
• State persistence
• Checkpointing

• Streaming fundamentals
• Watermarks and event time
• Streaming I/O patterns
• Streaming aggregation

• Unit testing transforms
• Integration testing pipelines
• Testing streaming pipelines
• Test data and fixtures

• Dataflow deployment
• Spark deployment
• Flink deployment
• Pipeline templates

• Pipeline monitoring
• Logging and debugging
• Performance optimization
• Error handling

The environment includes comprehensive sample datasets for hands-on learning:

• Sample Sales Data: Transaction records for pipeline processing
• Sample Streaming Data: Event data for streaming pipelines
• Sample Log Data: Server logs for ETL patterns
• Sample User Data: User behavior data for analytics

# Generate and load sample data
python3 scripts/generate_sample_data.py
python3 scripts/load_sample_data.py

Follow our recommended learning path:

1. Start with Fundamentals: Read Apache Beam Fundamentals wiki page
2. Set Up Environment: Follow Getting Started Guide
3. Begin Lab 0: Load sample data with Lab 0
4. Progress Through Labs: Follow the Learning Path

cd beam-code-practice
pip install -r requirements.txt
python3 scripts/setup.py

cd beam-code-practice
docker-compose up -d

• Python 3.8+ (for Python SDK)
• Java 11+ (for Java SDK)
• pip (Python package manager)
• 4GB RAM minimum (8GB recommended)
• 2GB disk space minimum

# Install Apache Beam
pip install apache-beam[gcp]

# Install additional runners
pip install apache-beam[gcp]  # For Dataflow
pip install apache-beam[spark]  # For Spark

import apache_beam as beam

# Set pipeline options
pipeline_options = {
    'runner': 'DirectRunner',
    'project': 'your-project',
    'region': 'us-central1',
    'temp_location': 'gs://your-bucket/temp'
}

Comprehensive wiki documentation is available with detailed guides:

• Wiki Home - Main wiki page with complete guide
• Lab 0: Sample Data Setup - Data generation and loading
• Lab 1: Environment Setup - Installation and configuration

Wiki Guides (Comprehensive learning materials):

• Wiki Home - Main wiki page with all guides
• Getting Started Guide - Complete setup and first steps
• Apache Beam Fundamentals - Core concepts and architecture
• Lab Guides - Detailed lab walkthroughs
• Learning Path - Recommended learning sequence
• Best Practices - Production-ready patterns
• Troubleshooting - Common issues and solutions

• Setup Guide - Detailed setup instructions for Python and Java
• Architecture Overview - System architecture and component details
• Pipeline Patterns - Common pipeline patterns and use cases
• Streaming Guide - Streaming processing concepts
• Deployment Guide - Production deployment strategies
• Lab Guide - Complete lab sequence and learning path
• Troubleshooting - Common issues and solutions

• Lab 0: Sample Data Setup - Generate and load sample data
• Lab 1: Environment Setup - Component verification and first pipeline
• Lab 2: Pipeline Fundamentals - Basic pipeline development
• Lab 3: Core Transforms - Transform functions and patterns
• Lab 4: I/O Connectors - Data sources and sinks
• Lab 5: Windowing and Triggers - Windowing strategies
• Lab 6: State and Timers - Stateful processing
• Lab 7: Streaming Pipelines - Streaming fundamentals
• Lab 8: Pipeline Testing - Testing strategies
• Lab 9: Production Deployment - Deployment to runners
• Lab 10: Monitoring and Operations - Production operations

Interactive Jupyter notebooks for hands-on learning:

• Lab Notebooks - Student notebooks with exercises
• Solution Helper - How to use the solution helper
• Notebook Helper - Guide for using notebooks effectively

• Setup Script - Environment validation and setup
• Generate Sample Data - Generate realistic pipeline data
• Load Sample Data - Load sample data for pipelines

Continue your learning journey with these related repositories:

• 🤖 DSPy Code Practice - Declarative LLM programming
• 🧠 LLM Fine-Tuning Practice - Model fine-tuning techniques

• 🦆 DuckDB Code Practice - Analytics & SQL optimization
• ⚡ Apache Spark Code Practice - Big data processing
• 🏔️ Apache Iceberg Code Practice - Lakehouse architecture

• ⚙️ Scala Data Analysis Practice - Functional programming

• 📚 Awesome My Notes - Comprehensive technical notes and learning resources

This environment uses only Apache 2.0 licensed tools:

• Apache Beam (Apache 2.0)
• Python packages (various open source licenses)
• Jupyter (BSD)
• Pandas (BSD)

No proprietary cloud services or consoles required.

This is a practice environment for learning. Feel free to extend labs, add examples, or improve the setup process.

Disclaimer: This is an independent educational resource for learning Apache Beam and modern data pipeline engineering. It is not affiliated with, endorsed by, or sponsored by Apache Beam or any vendor.

This repository is an open educational resource built for the data engineering community. We believe in learning together and sharing knowledge.

• 📖 Comprehensive Wiki: Detailed guides and tutorials for all skill levels
• 💬 GitHub Discussions: Ask questions and share insights with fellow learners
• 🐛 Issue Tracking: Report bugs and suggest improvements
• 🔄 Pull Requests: Contribute labs, fixes, and enhancements
• ⭐ Star the Repo: Show your support and help others discover this resource

We welcome contributions that improve the educational value:

• New Labs: Suggest new lab topics and exercises
• Better Explanations: Improve clarity of existing content
• Additional Examples: Add more practical examples
• Translation: Help translate content for global learners
• Bug Fixes: Report and fix issues in labs or documentation

See CONTRIBUTING.md for detailed contribution guidelines.

• Official Apache Beam Documentation: https://beam.apache.org/documentation/
• Apache Beam Blog: Latest updates and articles
• Conference Talks: Learn from industry experts

Apache License 2.0