Key Data Engineering Books to Level Up in 2025 | iCertGlobal

Data engineering is growing quickly. It’s important to stay updated on new trends, tools, and techniques for success in this field. In 2025, demand for data engineers will increase. About 11,500 new jobs will open each year until 2031 Reading good books on data engineering helps everyone, whether they're experts or beginners. This guide has it all. You’ll find basic ideas and advanced skills to help you stay ahead in this changing field.

Best Data Engineering Books for 2025

Fundamentals of Data Engineering – Joe Reis (2022)

This book is a great starting point for learning data engineering. It explains key topics like data modeling, ETL (Extract, Transform, Load), data pipelines, and data warehouses. It also teaches how to design strong and reliable data systems. If you want a solid foundation in data engineering, this book is a must-read for 2025!

Designing Data-Intensive Applications – Martin Kleppmann (2017)

This book helps you understand how big data applications work. It covers important topics like data storage, distributed computing, and data processing. Using real-life examples, it teaches you how to build strong and scalable data systems. If you want to work with large amounts of data, this book is a great choice.

The Data Warehouse Toolkit – Ralph Kimball

Ralph Kimball’s book is a top resource for learning how to design data warehouses. It explains simple but powerful methods for organizing data so it can be easily analyzed. The book has real-world examples and case studies. This makes it helpful for beginners and seasoned data engineers in 2025.

Big Data: Principles and Best Practices of Scalable Realtime Data Systems – James Warren (2015)

This book explains how real-time data systems collect, store, and process information. It covers key topics like distributed computing, stream processing, and real-time analytics. James Warren covers the challenges of big data. He shares ways to build systems that are quick, dependable, and able to grow.

Spark: The Definitive Guide – Matei Zaharia (2018) Matei Zaharia’s book is an excellent guide to Apache Spark. It’s a key tool for managing big data.

It describes how Spark operates. It covers distributed computing, data processing, machine learning, and real-time analytics. This book uses clear explanations and real-world examples. It helps readers learn how to use Spark for various big data tasks. It’s a must-read for anyone looking to learn Spark and use it to manage large amounts of data efficiently.

Data Science for Business – Tom Fawcett (2013)

This book teaches how data science can help businesses make smart decisions. Tom Fawcett covers important topics like data mining, predictive modeling, and machine learning. He also shows how companies use data to stay ahead of competitors. This book uses simple examples to show readers how to use data. Readers can solve real-world business problems with these lessons. It's a valuable tool for anyone wanting to use data for smarter business decisions in 2024 and beyond.

Data Engineering with Python – Paul Crickard (2020)

Paul Crickard's book offers a practical approach to using Python in data engineering.It covers key topics like creating data models, building ETL (Extract, Transform, Load) pipelines, and automating data processing. The book goes beyond theory. It offers real examples and Python code. Readers can use these tools to create their own data solutions. It emphasizes scalability and efficiency. This makes it a useful resource for anyone learning to manage large datasets with Python.

Data Mesh – Zhamak Dehghani (2021)

This book introduces Data Mesh, a new way to manage data in big companies. It encourages giving teams control over their own data instead of having it all in one spot. This helps companies scale, organize, and use data more efficiently. The book discusses the challenges of using this system. It also shares real-world examples to help businesses switch. It’s a great read for data engineers and architects looking to modernize data systems in 2025.

Preparation Tips for Data Engineering

Getting ready for a data engineering job requires both technical skills and hands-on experience. Here are some tips to help you prepare:

1. Focus on these programming languages: Python, Java, Scala, and SQL. They are popular in data engineering. Practice writing clean, efficient code for handling and processing data.

2. Get Familiar with Data Technologies : Get to know popular tools like Apache Hadoop, Apache Spark, and Kafka. Also, look into various databases, such as SQL and NoSQL.

Understand how they work and how they fit into data pipelines.

3. Understand Data Modeling: Build a strong foundation in data modeling techniques such as dimensional modeling, entity-relationship modeling, and schema design. Organizing data properly makes it easier to analyze.

4. Work on Real Projects : Practice with real-world projects to gain hands-on experience. Try building data pipelines, writing ETL scripts, and working with data warehouses. You can also join online competitions to improve your skills.

5. Stay Updated : The world of data engineering changes fast. So, keep learning about new tools and techniques. Follow industry blogs, join online forums, attend webinars, and connect with other data engineers to stay ahead.

6. Improve Soft Skills : Besides technical skills, communication, problem-solving, and teamwork are important. Data engineers work with various teams. They need to explain technical ideas to non-technical people. This skill is very important.

Follow these steps to get ready for a successful career in data engineering

More Ways to Learn Data Engineering

• Online Courses and Tutorials Take courses online from iCert Global. They can help you boost your programming skills.
• These courses offer lessons on basic and advanced data engineering. You will learn with videos, do assignments, and tackle projects.
• Books and Reading Materials Read books and blogs by data engineering experts.

Some great books are:

• Designing Data-Intensive Applications by Martin Kleppmann
• Data Engineering Teams by Dave Holtz

1. Open Source Projects Join open-source projects on sites like GitHub. Working with other developers on real projects helps you gain experience. It also lets you demonstrate your skills to employers.
2. Competitions Compete in data challenges on platforms like Kaggle. These contests let you tackle real-world problems. You’ll work with big data and build teamwork skills.
3. Networking and Communities: Join online forums like LinkedIn. Connect with other data engineers. Ask questions, share ideas, and learn from others.
4. Bootcamps and Workshops Join bootcamps and workshops hosted by tech companies or universities. These programs give you hands-on training, expert mentorship, and networking opportunities.

How to obtain Big Data certification? 

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The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Data engineering is a smart career choice. It’s especially strong in technology, finance, healthcare, and e-commerce. Learning the right skills and getting real-world experience will help you succeed. One great way to build these skills is by joining the Post Graduate Program in Data Engineering. This course teaches everything from basic concepts to advanced techniques in big data. You'll tackle real-world projects and case studies. You'll also learn from experts about tools like Hadoop, Spark, and Kafka.

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