Best Data Engineering Books (For Aspiring + Experienced DEs)

Data engineering is one of the hottest jobs on the market today. With only a few years of experience, data engineer salaries can be as high as $200K per year. In the last few years, the salary for data engineers has slowly caught up with the salary for software engineers. If you are an aspiring data engineer or a seasoned DE, you will benefit from the books below.

Fundaments of Data Engineering

Fundaments of Data Engineering provides a solid foundation for data engineers with an emphasis on the principles behind the fast-growing discipline. The book covers the data engineering life cycle in-depth. I really enjoyed reading the sections on data architecture, storage. , and serving data for data scientists, machine learning engineers, and reverse ETL. I referred data engineers to Martin Kleppmann’s Designing Data-Intensive Applications book for years but the Fundamentals of Data Engineering is now my go-to recommendation on the topic.

Designing Data-Intensive Applications

This book is a must-read for any data engineer. The author Martin Kleppmann does a fantastic job of starting with the basics and taking readers all the way through complex topics. The book includes the basics of data systems (databases, indexes, etc), distributed data, and derived data (stream processing). This book will improve your understanding of data engineering problems and help you develop suggestions. A drawback of this book is that it doesn’t have a lot of code examples.

It’s a fairly long book that will take you a long time to read. So feel free to skip parts that are not of interest to you. But a full immersive reading will help you get the most out of this book.

Data Pipelines Pocket Reference: Moving and Processing Data for Analytics

This is a very practice book with a focus on ETL and ELT systems and data processing. As the name suggests, it’s a pocket reference book – the topics are covered briefly. If you need in-depth reading, you will need to complement this book with additional reading. The book covers the often overlooked part of moving data – testing and data quality.

The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling

This book is a classic that everyone working with a data warehouse should read. Whether you are a Business Intelligence engineer, data analyst, or business user, the case studies covered in this will book will resonate with you. The author Ralph Kimball introduced dimensional modeling and is considered the father of the modern data warehouse. This book includes everything you need to know about using dimension and fact tables and best practices for big data analytics.

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