DataWorks & MaxCompute Practice Project

A comprehensive, production-ready learning repository for Alibaba Cloud DataWorks and MaxCompute with realistic sample data, ETL workflows, and advanced features.

🎯 Project Status: 100% Core Complete ✅

This project provides a complete learning environment with:

• Realistic sample datasets for hands-on practice
• Comprehensive SQL examples from basic to advanced operations
• Complete ETL framework with data quality monitoring
• Custom UDF examples for Java and Python
• Automated workflow orchestration with DataWorks
• Professional documentation and troubleshooting guides

📁 Project Structure

dataworks-maxcompute-practice/
├── data/                     # Sample datasets (8 files)
│   ├── customers.csv         # Customer master data
│   ├── products.csv          # Product catalog  
│   ├── orders.csv            # Order transactions
│   ├── order_items.csv       # Order line items
│   ├── web_sessions.csv      # User session analytics
│   ├── page_views.csv        # Page view events
│   ├── user_events.csv       # Custom interaction events
│   └── suppliers.csv         # Supplier information
├── sql/                      # MaxCompute SQL scripts (6 scripts)
│   ├── 01_create_tables.sql  # Table creation and schemas
│   ├── 02_load_data.sql      # Data loading techniques
│   ├── 03_basic_queries.sql  # Basic SQL operations
│   ├── 04_joins_analytics.sql # Advanced analytics
│   ├── 05_etl_workflows.sql  # ETL transformation patterns
│   └── 06_data_quality.sql   # Data quality framework
├── workflows/                # DataWorks workflow definitions
│   └── daily_etl_workflow.json # Complete ETL orchestration
├── udf/                      # User Defined Functions
│   ├── java/StringUtils.java # String processing utilities
│   └── python/text_analytics.py # NLP and text analysis
├── scripts/                  # Utility scripts
│   └── data_generator.py     # Generate large-scale test data
├── docs/                     # Documentation and guides
│   ├── getting_started.md    # Comprehensive setup guide
│   └── troubleshooting.md    # Common issues and solutions
├── tests/                    # Test scripts
│   └── test_queries.sql      # Comprehensive validation tests
├── TODO.md                   # Project completion status
└── README.md                 # This file

🚀 Quick Start (15 minutes)

1. Prerequisites

• Alibaba Cloud account with DataWorks and MaxCompute enabled
• Basic SQL knowledge
• Access to DataWorks console

2. Setup Your Environment

# Clone the repository
git clone https://github.com/rcdelacruz/dataworks-maxcompute-practice.git
cd dataworks-maxcompute-practice

# Follow the detailed setup guide
open docs/getting_started.md

3. Create Tables and Load Data

1. Run table creation scripts: sql/01_create_tables.sql
2. Load sample data: sql/02_load_data.sql
3. Verify installation: tests/test_queries.sql

4. Start Learning

Begin with Module 1 and progress through the learning path outlined in the getting started guide.

📊 Sample Datasets

E-commerce Data (Complete Transactional System)

• customers.csv: Customer master data with demographics
• products.csv: Product catalog with pricing and categories
• orders.csv: Order transactions with status tracking
• order_items.csv: Detailed line items with quantities and pricing
• suppliers.csv: Supplier information and ratings

Web Analytics Data (Complete Digital Analytics)

• web_sessions.csv: User session data with device and traffic source info
• page_views.csv: Page view events with timing and referrer data
• user_events.csv: Custom interaction events (clicks, scrolls, forms)

Data Relationships

All datasets are interconnected with proper foreign key relationships, enabling realistic join operations and complex analytics scenarios.

🎓 Learning Modules

Module 1: Basic Operations (1-2 hours)

Objective: Master fundamental MaxCompute SQL operations

• Table creation and schema design
• Data loading techniques and best practices
• Basic queries, filtering, and aggregations
• Files: sql/01_create_tables.sql, sql/02_load_data.sql, sql/03_basic_queries.sql

Module 2: ETL Workflows (2-3 hours)

Objective: Build production-grade ETL pipelines

• Multi-table joins and complex analytics
• Data quality checks and cleansing
• Incremental processing patterns
• Files: sql/04_joins_analytics.sql, sql/05_etl_workflows.sql, sql/06_data_quality.sql

Module 3: Advanced Features (3-4 hours)

Objective: Implement custom functions and advanced analytics

• User Defined Functions (Java and Python)
• Text processing and analytics
• Performance optimization techniques
• Files: udf/java/StringUtils.java, udf/python/text_analytics.py

Module 4: DataWorks Integration (2-3 hours)

Objective: Master workflow orchestration and automation

• Workflow scheduling and dependencies
• Error handling and monitoring
• Production deployment patterns
• Files: workflows/daily_etl_workflow.json

🛠️ Advanced Features

Custom UDF Examples

• Java StringUtils: Comprehensive string manipulation and validation functions
• Python Text Analytics: NLP processing including sentiment analysis and keyword extraction

ETL Framework

• Data Quality Monitoring: Automated quality checks with alerting
• Incremental Processing: Change data capture and delta processing patterns
• Performance Optimization: Query optimization and cost management

Workflow Orchestration

• Dependency Management: Complex workflow dependencies with error handling
• Monitoring & Alerting: SLA monitoring with automated notifications
• Resource Management: Memory and CPU optimization configurations

🧪 Data Generation

Generate large-scale datasets for performance testing:

# Generate 10,000 customers
python scripts/data_generator.py --table customers --records 10000

# Generate all tables with 5,000 records each
python scripts/data_generator.py --table all --records 5000

# Generate 100,000 web sessions for specific date range
python scripts/data_generator.py --table web_sessions --records 100000

📚 Documentation

Essential Guides

• Getting Started Guide: Comprehensive setup and learning path
• Troubleshooting Guide: Common issues and solutions
• Project Status: Current completion status and future enhancements

SQL Reference

All SQL scripts include detailed comments explaining:

• MaxCompute-specific syntax and functions
• Best practices and optimization techniques
• Real-world use case scenarios
• Expected outputs and results

🔧 Testing and Validation

Comprehensive Test Suite

Run the complete validation suite to verify your setup:

-- Execute all tests
@sql/tests/test_queries.sql

-- Verify data quality
@sql/06_data_quality.sql

Test Coverage

• Data integrity and referential consistency
• Query performance and optimization
• UDF functionality and error handling
• Workflow execution and dependency management

🌟 Key Features

Production-Ready Components

• ✅ Realistic Data Models: Based on real e-commerce and analytics patterns
• ✅ Complete ETL Framework: Industry-standard data processing patterns
• ✅ Quality Assurance: Comprehensive data quality and testing framework
• ✅ Performance Optimized: Query optimization and cost management examples
• ✅ Scalable Architecture: Designed for both learning and production use

Learning-Focused Design

• ✅ Progressive Complexity: From basic to advanced concepts
• ✅ Hands-on Examples: Practical exercises with real business scenarios
• ✅ Best Practices: Industry-standard patterns and techniques
• ✅ Comprehensive Documentation: Detailed guides and troubleshooting

🚀 Future Enhancements (Optional)

While the core project is 100% complete, these optional enhancements could extend its capabilities:

Advanced SQL Examples

• Performance optimization patterns
• Machine learning feature preparation
• Real-time processing examples
• Security and permissions

Additional Workflows

• Customer segmentation automation
• ML model training pipelines
• Real-time analytics processing

Extended Utilities

• Performance monitoring dashboard
• Cost optimization analyzer
• Automated backup solutions

See TODO.md for the complete list of optional enhancements.

🤝 Contributing

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

Areas for Contribution

• Additional sample datasets
• More UDF examples
• Advanced workflow patterns
• Documentation improvements
• Performance optimization examples

Contribution Process

1. Fork the repository
2. Create a feature branch
3. Add your improvements
4. Test thoroughly
5. Submit a pull request

📞 Support and Community

Getting Help

• Documentation: Check the comprehensive guides in /docs
• Issues: Open GitHub issues for questions or problems
• Discussions: Use GitHub Discussions for general questions

Professional Support

• Alibaba Cloud Technical Support
• DataWorks Consulting Services
• MaxCompute Training Programs

📈 Success Metrics

After completing this project, you'll be able to:

• ✅ Design and implement scalable data warehouse solutions
• ✅ Build production-grade ETL pipelines with monitoring
• ✅ Optimize query performance and manage costs effectively
• ✅ Develop custom functions for specialized data processing
• ✅ Orchestrate complex workflows with proper error handling
• ✅ Implement comprehensive data quality frameworks

🏆 Project Achievements

Completion Status: 100% Core Complete ✅

• 8/8 Core datasets implemented with realistic data
• 6/6 Priority SQL scripts covering basic to advanced operations
• 2/2 Priority UDF examples for Java and Python
• 1/1 Priority workflow with complete orchestration
• Professional documentation with comprehensive guides
• Data generation tools for scaling test environments

Ready for Production Use

This project is production-ready for learning, training, and development environments, providing everything needed for comprehensive DataWorks & MaxCompute education.

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📝 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

• Alibaba Cloud Documentation Team for technical references
• DataWorks and MaxCompute engineering teams for platform capabilities
• Open source community for best practices and patterns

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Start your DataWorks & MaxCompute journey today! 🚀

Follow the Getting Started Guide to begin learning with hands-on, realistic scenarios.