The Best Books for Data Engineers

Updated on: May 6, 2024

Getting tired of the endless screen glare? It might be a good time to switch to a book. Dive into our selected list of books for data engineers, perfect for expanding your insights on databases. It’s a refreshing change from the digital routine!

Last time, I shared a list of the best books to learn SQL. This time, I want to introduce a few books for data engineers. They are worth reading and will help you learn more about databases.

As you might know, I usually write about the advantages of online learning. But to appreciate online learning, it is sometimes necessary to get away from it.

Also, many of us now work from home and probably spend a lot of time on our computers and smartphones. This makes it more important to vary how we learn and work.

Reading books (especially paper ones) is the best way for me to focus and relax at the same time. Make sure to choose your favorite way to read. Sit in a comfortable chair and grab a book you find interesting and worth reading.

If you would like inspiration, check out my recommendations below. Each book title is linked to Amazon to help you find it.

1.  Big Data for Dummies

I’m sorry for listing this book first, but when I found it, I knew it was for me. And I’m sure that many people who are new to the data engineering world may sometimes (wrongly!) feel like dummies.

This book is perfect for anyone who is just learning what it is to be a data engineer. And it will effectively guide you through complex and often confusing subjects.

You will go from being a bit lost to becoming more confident and understanding the basics needed to develop your skills. This is important since big data tools are at the core of data engineering.

Big Data for Dummies covers big data tools and how to use big data in business. You will learn how to integrate structured and unstructured data into your big data environment and how to use predictive analytics to make better decisions.

Here are the basic topics found inside:

• Profiles of various available technologies
• The role of the cloud
• How MapReduce aids big data management
• Specific uses for text analytics
• How to approach big data security and privacy
• Ten best practices for managing big data

2.  Big Data Black Book

“The Big Data Black Book (Covers Hadoop 2, MapReduce, Hive, YARN, Pig, R, and Data Visualization)” is another good book for beginners. It gives you the big picture, which is great for someone starting to learn about data engineering tools.

It covers all of the basic knowledge for data engineers. Although it is not a book for professionals, it will give you an overview to help you start your career in the big data world.

You will find these topics inside:

• Big data in the business context
• The Hadoop ecosystem
• MapReduce fundamentals
• Big data technologies
• Data processing with MapReduce
• YARN, Hive, and Pig
• Data manipulation, functions, and packages
• Graphical analyses using R
• Big data visualization techniques

3. Fundamentals of Data Engineering

"Fundamentals of Data Engineering" by Joe Reis and Matt Housley feels like a must-have for anyone diving into the expansive world of data engineering.

Right from the start, the authors break down what data engineering actually entails—the art and science of turning raw data into clean, analysis-ready information. They walk you through the entire lifecycle of data engineering, highlighting the critical stages: generation, ingestion, orchestration, transformation, and serving.

With a mix of personal stories and practical examples, Joe and Matt make the technical content not just understandable but also relevant and engaging. This storytelling approach helps everyone from experienced engineers to beginners connect with the practical aspects of the field in a truly meaningful way.

The book's structure seamlessly guides you from one chapter to the next, progressively building your knowledge and boosting your confidence.

The book gazes into the future of Data Engineering, blending insights on current trends with forecasts that could redefine data engineering. This forward-thinking angle not only educates but also offers a glimpse into where the industry might be headed.

Whether you’re looking to sharpen your skills or just starting to get curious about data engineering, "Fundamentals of Data Engineering" delivers a comprehensive education and a preview of what’s to come.

4.  Designing Data-Intensive Applications

The cover of this book might look familiar to you. In the article about SQL books mentioned above, we recommended another O'Reilly book.

“Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems” is for those who already have some experience in building web-based applications or network services. You should also be familiar with relational databases and SQL.

This book will especially help software engineers, software architects, and technical managers. However, it is for anyone passionate about coding.

Martin Kleppmann gives a problem-solving approach. If you are struggling with something and don’t know which tools to use, this book will help you understand the pros and cons of your options.

You will not find detailed instructions on how to use software packages. Instead, the author discusses various fundamental principles for data systems. He looks at the architecture of data systems and the ways they are integrated into data-intensive applications.

5.  Data-Driven Science and Engineering

“Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control” is about the fascinating world of data science. It brings together machine learning, engineering mathematics, and mathematical physics.

Brunton and Kutz wrote this book for graduate students and advanced researchers. However, anyone interested in this field would enjoy this book.

You will find this information:

• In-depth examples with comprehensive code
• Digestible, accessible explanations of complex concepts

Online supplements with exercises, homework, case studies, and supplementary code

Readers will be guided through difficult concepts with ease. This well-written book gives clear examples that help beginners understand the subject. The authors also provide figures with detailed captions and sample code for most of the examples.

Although there are features to help beginners, more experienced researchers will find satisfaction with the advanced methods presented in this book.

6.  The Data Science Handbook

And last but not least, “The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists.” This book is more for relaxation and inspiration.

It is about data science in general rather than just data engineering. Inside, you will find 25 interviews with the world’s best data scientists.

Experts from established companies (including Facebook, LinkedIn, Pandora, Intuit, The New York Times) and fast-growing startups (including Uber, Airbnb, Mattermark, Quora, Square, and Khan Academy) share their life and work experiences. You will learn about their career paths and strategies for achieving goals and success.

These interviews include what the experts learned and the mistakes they made. You can then use their helpful tips for working in a data environment.

This book does not concentrate on the technical aspects of data science. Instead, it focuses on practical insight and advice. What a great opportunity to learn from the best!

7. Data Engineering And AI For Beginners

This cool book by William Leeson is your entry point into the dynamic interplay between artificial intelligence (AI) and data engineering.

The author simplifies the intricate details, focusing on how AI can revolutionize traditional tasks in data engineering. And it doesn’t just stop at the theoretical aspects; it dives deep into practical applications, showcasing how data engineering underpins AI-driven analytics and decision-making processes.

Through real-life examples, Leeson illustrates how data engineers and AI experts come together to build robust, scalable data infrastructures, making it easier for beginners to see the practical side of their learning.

Leeson’s engaging and clear style makes even the most complex topics approachable for newcomers, without skimming on the important details.

For those new to the evolving fields of data engineering and AI, this book is an invaluable guide, packed with insights that are as practical as they are innovative.

What Do You Have On Your Bookshelf?

This collection of books can be another source of knowledge for you. It can help you better understand the basics and develop your skills. The more ways you learn, the more advanced you can become.

Have you read any of the books I recommended? Do you have another favorite or must-read list to share? Please write your thoughts and recommendations in the comments below and share your experience with others.