Books of Data Engineering

Last updated Feb 9, 2024

• Data Engineering Design Patterns (DEDP) - My free book :)
• Fundamentals of Data Engineering, Joe Reis, Matt Housley
• The Data Warehouse Toolkit, Ralph Kimball: February 1996, The guide for data warehouse modeling and still relevant to this day.
• Building the Data Warehouse, Bill Inmon
• Data Modeling with Snowflake, Serge Gershkovich
• The Data Engineering Cookbook, Andreas Kretz
• Data Engineering Patterns on the Cloud, Bartosz Konieczny
• Introduction to Data Engineering, Daniel Beach
• Data With Rust, Karim Jedda
• Data Pipelines Pocket Reference, James Densmore
• Designing Data-Intensive Applications
• DAMA-DMBOK: Data Management Body of Knowledge (DAMA-DMBOK)
• Streaming Systems, Tyler Akidau, Slava Chernyak, Reuven Lax
• High Performance Spark, Holden Karau, Rachel Warren
• Data Pipelines with Apache Airflow
• Fundamentals of Data Observability, Andy Petrella
• Scaling Machine Learning with Spark, Adi Polak
• Deciphering Data Architectures, James Serra (Deciphering Data Architectures (James Serra))
• Architecture Patterns with Python
• Learning Spark, Brooke Wenig, Denny Lee, Tathagata Das, Jules Damji
• The Unified Star Schema. Bill Inmon

3. Design Patterns(Erich Gamma, Richard Helm, October 1994, ISBN-10: 0201633612) - The mother of Design Patterns, general use but not dedicated to Data Engineering.
4. System Design Interview (Alex Xu, Sahn Lam, Volume 2 March 2022, ISBN-10: 0201633612) - This book seems to be a great general approach to designs and patterns, but it doesn’t include Data Engineering.

See also People of Data Engineering.

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Created 2023-12-08