SQL for Data Engineering Learning Project

This repository is my personal learning journey to master SQL for Data Engineering. The focus is on practical, production-relevant SQL skills that frequently appear in data engineering job requirements and real-world ETL tasks.

🎯 Goal

Build strong, practical SQL skills for data engineering roles through hands-on practice with real-world scenarios.

🗺️ Learning Roadmap

Phase 1: Core Fundamentals

• JOINs (INNER, LEFT, handling duplicates)
• Aggregations with GROUP BY
• Common Table Expressions (CTEs)
• CASE statements for data cleaning

Phase 2: Advanced Transformations

• Window functions (RANK, ROW_NUMBER, LAG/LEAD, running totals)
• Subqueries vs JOINs
• Handling NULLs and data quality
• Date/time manipulation

Phase 3: Production Patterns

• Incremental loading
• Deduplication techniques
• Idempotent queries
• Basic query optimization

📁 Repository Structure

.
├── phase1-core-fundamentals/
├── phase2-advanced-transformations/
├── phase3-production-patterns/
├── datasets/
├── exercises/
└── README.md

Directory Details

phase1-core-fundamentals/ Foundation concepts and basic operations

phase2-advanced-transformations/ Complex data manipulation techniques

phase3-production-patterns/ Best practices for production code

datasets/ Sample data files for practice

exercises/ Hands-on practice problems

README.md Project documentation

What Each SQL File Contains

• ✅ Clear explanations
• ✅ Sample data
• ✅ Example queries
• ✅ Notes on best practices

🚀 How to Use

1. Clone the repository

   git clone https://github.com/your-username/your-repo-name.git

2. Choose your SQL environment

   • PostgreSQL
   • BigQuery
   • Snowflake
   • DB Fiddle
   • Or any SQL environment you prefer

3. Start learning

   • Navigate to Phase 1 to begin
   • Open SQL files in your chosen environment
   • Run and experiment with the queries
   • Complete exercises to reinforce learning

💡 Learning Approach

• Hands-on practice - Every concept includes runnable examples
• Real-world scenarios - Problems mirror actual data engineering tasks
• Progressive difficulty - Build skills incrementally from fundamentals to advanced patterns
• Production-focused - Learn patterns used in professional environments

🛠️ Technologies

This repository focuses on standard SQL that works across:

• PostgreSQL
• MySQL
• BigQuery
• Snowflake
• Redshift

Platform-specific syntax is noted where applicable.

📈 Progress Tracking

• Phase 1: Core Fundamentals
• Phase 2: Advanced Transformations
• Phase 3: Production Patterns

🤝 Contributing

This is a personal learning project, but suggestions and improvements are welcome! Feel free to:

• Open an issue for corrections or improvements
• Submit a pull request with additional examples
• Share your own learning experiences

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Happy Learning! 🚀

Building data engineering skills one query at a time.