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The Cloud Data Engineer

Master data engineering from foundations to cloud.

This book is organized as a complete learning path for modern data engineering. It starts with the mindset and systems view of the profession, then builds through SQL, Python, orchestration, engineering practices, AI-assisted workflows, cloud warehousing, streaming, AWS, and Azure.

Book Path​

  1. Front Matter
  2. Part 0 - Thinking Like a Data Engineer
  3. Part 1 - SQL: The Language of Data
  4. Part 2 - Python: The Engineer's Tool
  5. Part 3 - Orchestration: Apache Airflow
  6. Part 4 - Engineering Practices
  7. Part 5 - Agentic Data Engineering
  8. Part 6 - Cloud Warehousing: Snowflake + dbt
  9. Part 7 - Real-Time Data: Apache Kafka
  10. Part 8 - AWS for Data Engineers
  11. Part 9 - Azure for Data Engineers
  12. Appendices

How to Use This Book​

Read the parts in order if you are building from foundations. If you already work with data systems, use the sidebar as a reference map and jump to the chapters that match your current project.

Each chapter page is structured to grow into a full lesson with concepts, examples, pitfalls, exercises, and production context.