5 Best Books for Mastering Data Engineering

Written by James Densmore, this compact guide provides an excellent introduction to data pipelines and their critical role in data analytics. It dives into how data from diverse sources is moved and transformed to provide value, distinguishing between having data and genuinely gaining value from it.

The book defines what a data pipeline is and how it functions in modern data infrastructure, including cloud platforms. It gives you an understanding of the common tools and products used by data engineers to build pipelines, and how these pipelines support analytics and reporting needs. It’s a great place to start or to use as a quick reference when dealing with data pipeline challenges.

Authored by Barr Moses, Lior Gavish, and Molly Vorwerck, this book addresses one of the biggest challenges in data engineering: ensuring data quality. The authors, who are from the data observability company Monte Carlo, present a practical guide on how to tackle data quality and trust issues at scale.

The book discusses how to build more trustworthy and reliable data pipelines, use scripts for data checks, identify broken pipelines, and set and maintain data SLAs, SLIs, and SLOs. By reading this book, you’ll learn how to treat data services and systems with the diligence of production software and automate data lineage graphs across your data ecosystem. The insights offered in this book will help you overcome the “good pipelines, bad data” problem that many data engineering teams face.

Sameer Farooqui, the author of this guide, provides a collection of Spark best practices, updates, and step-by-step recipes to address new use cases and broaden your skills. He explains how to write Spark applications using best practices, how to use SparkSQL for static data analysis, and how to analyze real-time data with Structured Streaming.

With its focus on Apache Spark, one of the most popular open-source projects for large-scale data processing, this book is a valuable resource for data engineers working in big data environments. Farooqui’s insights will help you understand how to leverage Spark to make your data processing tasks more efficient and effective.

We’re still gathering more detailed information on this book. However, given the importance of cloud computing and AWS (Amazon Web Services) in modern data engineering, this book is certainly a valuable addition to any data engineer’s library.

The title suggests that it provides practical knowledge on how to design and build data transformation pipelines in the cloud using AWS. This knowledge is crucial as more and more companies move their data infrastructure to the cloud to take advantage of its scalability, reliability, and cost-effectiveness.

This is the ultimate book on our list. Written by data engineering experts Joe Reis and Matt Housley, it dives deep into the core principles of data engineering, providing the fundamental knowledge needed to plan and build robust data systems. This book is an end-to-end masterpiece that will arm you for a career in this field.

With its comprehensive coverage of topics, this book serves as a go-to guide for both beginners.