Data engineering is a critical domain within the data ecosystem. It focuses on the architecture, development, and maintenance of data pipelines, data warehouses, and real-time processing systems.
As organizations increasingly adopt big data and cloud technologies, data engineers play a central role in enabling analytics, business intelligence, and machine learning operations. They ensure that raw data is clean, accessible, and efficiently processed for downstream applications.
Mastering data engineering requires knowledge of ETL processes, distributed computing, data modeling, and orchestration tools like Apache Airflow and Spark. Understanding systems design and cloud infrastructure is equally important.
Best Data Engineering Books
To help professionals grow in this field, we’ve compiled a list of the best data engineering books—resources that reinforce core concepts and align with real-world technical challenges.
Fundamentals of Data Engineering: Plan and Build Robust Data Systems
- Reis, Joe (Author)
- English (Publication Language)
- 447 Pages - 07/26/2022 (Publication Date) - O'Reilly Media (Publisher)
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
- Kleppmann, Martin (Author)
- English (Publication Language)
- 611 Pages - 05/02/2017 (Publication Date) - O'Reilly Media (Publisher)
Databricks Certified Data Engineer Associate Study Guide: In-Depth Guidance and Practice
- Alhussein, Derar (Author)
- English (Publication Language)
- 408 Pages - 03/25/2025 (Publication Date) - O'Reilly Media (Publisher)
Data Pipelines Pocket Reference: Moving and Processing Data for Analytics
- Densmore, James (Author)
- English (Publication Language)
- 274 Pages - 03/16/2021 (Publication Date) - O'Reilly Media (Publisher)
Financial Data Engineering: Design and Build Data-Driven Financial Products
- Khraisha, Tamer (Author)
- English (Publication Language)
- 504 Pages - 11/12/2024 (Publication Date) - O'Reilly Media (Publisher)
Data Engineering Best Practices: Architect robust and cost-effective data solutions in the cloud era
- Schiller, Richard J. (Author)
- English (Publication Language)
- 550 Pages - 10/11/2024 (Publication Date) - Packt Publishing (Publisher)
Architecting Data Solutions with Snowflake: Design scalable, cloud-native data platforms for analytics, warehousing, and beyond (English Edition)
- Kelgaonkar, Pooja (Author)
- English (Publication Language)
- 352 Pages - 08/07/2025 (Publication Date) - BPB Publications (Publisher)
AI Engineering: Building Applications with Foundation Models
- Huyen, Chip (Author)
- English (Publication Language)
- 532 Pages - 01/07/2025 (Publication Date) - O'Reilly Media (Publisher)
Data Engineering with AWS: Acquire the skills to design and build AWS-based data transformation pipelines like a pro
- Eagar, Gareth (Author)
- English (Publication Language)
- 636 Pages - 10/31/2023 (Publication Date) - Packt Publishing (Publisher)
Data Engineering for Cybersecurity: Build Secure Data Pipelines with Free and Open-Source Tools
- Bonifield, James (Author)
- English (Publication Language)
- 344 Pages - 08/26/2025 (Publication Date) - No Starch Press (Publisher)
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
- Hardcover Book
- Brunton, Steven L. (Author)
- English (Publication Language)
- 614 Pages - 07/28/2022 (Publication Date) - Cambridge University Press (Publisher)
Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python
- Crickard, Paul (Author)
- English (Publication Language)
- 356 Pages - 10/23/2020 (Publication Date) - Packt Publishing (Publisher)
Data Engineering with dbt: A practical guide to building a cloud-based, pragmatic, and dependable data platform with SQL
- Zagni, Roberto (Author)
- English (Publication Language)
- 578 Pages - 06/30/2023 (Publication Date) - Packt Publishing (Publisher)
Data Engineering with Databricks Cookbook: Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake
- Chadha, Pulkit (Author)
- English (Publication Language)
- 438 Pages - 05/31/2024 (Publication Date) - Packt Publishing (Publisher)
AWS Certified Data Engineer Study Guide: Associate (DEA-C01) Exam (Sybex Study Guide)
- Humair, Syed (Author)
- English (Publication Language)
- 656 Pages - 03/25/2025 (Publication Date) - Sybex (Publisher)
Ace the Data Engineering Interview: Questions and Answers for Python, SQL, Data Modeling and More
- Amazon Kindle Edition
- Coyne, Sean (Author)
- English (Publication Language)
- 315 Pages - 03/05/2025 (Publication Date)
Snowflake Data Engineering
- Ferle, Maja (Author)
- English (Publication Language)
- 368 Pages - 01/28/2025 (Publication Date) - Manning (Publisher)
Data Engineering Design Patterns: Recipes for Solving the Most Common Data Engineering Problems
- Amazon Kindle Edition
- Konieczny, Bartosz (Author)
- English (Publication Language)
- 614 Pages - 05/09/2024 (Publication Date) - O'Reilly Media (Publisher)
Introduction to Data Engineering : A Beginner’s Guide to Data Pipelines and Big Data Tools
- Amazon Kindle Edition
- BLUNT, BOOKER (Author)
- English (Publication Language)
- 131 Pages - 05/26/2025 (Publication Date)
Data Engineering Foundations: Core Techniques for Data Analysis with Pandas, NumPy, and Scikit-Learn (Advanced Data Analysis Series)
- Technologies, Cuantum (Author)
- English (Publication Language)
- 594 Pages - 11/06/2024 (Publication Date) - Staten House (Publisher)
The responsibilities of a data engineer continue to evolve with the rise of data lakes, streaming platforms, and hybrid cloud environments. Staying up to date with scalable architectures and tools is essential for long-term success.
Whether you’re building data pipelines with Python, optimizing SQL queries, or managing workflows on cloud-native platforms, developing a strong technical foundation is key.
The best data engineering books support this journey by offering structured insights, practical examples, and systems-level thinking. Use them to advance your skills, align with industry trends, and contribute to robust, data-driven solutions.
