Are you looking for Best Data Engineering Books?. If yes, then your search will end here. In this article, I have listed the 7 Best Data Engineering Books. So read the full article and find out the best book for you.
Data Engineer is a person who is responsible for managing data workflows, pipelines, and ETL processes. As the name suggests, “Data Engineering”, which means it is associated with data, namely, their delivery, storage, and processing.
The most demanding skills or technologies for Data engineers are SQL, Python, Spark, AWS, etc. Books are very important to learn these skills because books provide a solid understanding of the concepts. So without any further ado, let’s start finding Best Data Engineering Books.
Check-> 12 Best Data Engineering Courses
Best Data Engineering Books
- 1. Data Engineering with Python
- 2. Designing Data-Intensive Applications
- 3. Spark: The Definitive Guide: Big Data Processing Made Simple
- 4. Data Science For Dummies
- 5. The Data Warehouse Toolkit
- 6. Building a Data Warehouse: With Examples in SQL Server
- 7. Big Data: Principles and best practices of scalable real-time data systems
1. Data Engineering with Python
Author- Paul Crickard
About Book-
This book provides a clear understanding of data modeling techniques and pipelining. At the beginning of the book, you will learn the basics of data engineering. Then you will learn the technologies and frameworks required to build data pipelines to work with large datasets.
You will also learn how to transform and clean data and perform analytics to get the most out of your data. At the last of the book, you will learn how to work with big data of varying complexity and production databases, and build data pipelines. You will also build architectures on which you’ll learn how to deploy data pipelines using real-world examples.
Where to Buy this Book?
You can buy this book on amazon- Data Engineering with Python
2. Designing Data-Intensive Applications
Author- Martin Kleppmann
About Book-
This is a practical and comprehensive guide. This book deals with all the stuff that happens around data engineering like storage, models, structures, access patterns, encoding, replication, partitioning, distributed systems, batch & stream processing, and the future of data systems.
By reading this book, you get a clear understanding of real-world big data architecture. This book is good for you if you are working on or interviewing for big data engineering. This book provides an amazing introduction to the fundamental concepts behind the much-hyped Big Data tools.
Where to Buy this Book?
You can buy this book on amazon- Designing Data-Intensive Applications
3. Spark: The Definitive Guide: Big Data Processing Made Simple
Author- Bill Chambers, Matei Zaharia
About Book-
Apache Spark is a powerful platform for Big Data applications. This book clearly describes Spark architecture and has a lot of outstanding examples. The code presented in this book and provided in the accompanying notebooks is Python, Scala, and Spark SQL. This is a good book for Spark enthusiasts.
Where to Buy this Book?
You can buy this book on amazon- Spark: The Definitive Guide: Big Data Processing Made Simple
4. Data Science For Dummies
Author- Lillian Pierson, Jake Porway
About Book-
This book focuses on Business cases. That’s why this book explains big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. This book will help you to pick up the skills you need to begin a new career or initiate a new project.
After reading this book, you will have background knowledge of Big Data and Data Engineering. You will also learn big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL.
Where to Buy this Book?
You can buy this book on Amazon-Data Science For Dummies
5. The Data Warehouse Toolkit
Author- Ralph Kimball, Margy Ross
About Book-
This book provides a good comprehensive overview and up to date with current practice and includes a clear discussion of newer topics such as big data. This book also covers new and enhanced star schema dimensional modeling patterns.
There are two new chapters in this book on ETL techniques. Overall this is a good book for understanding how data warehouses work.
Where to Buy this Book?
You can buy this book on Amazon- The Data Warehouse Toolkit
6. Building a Data Warehouse: With Examples in SQL Server
Author- Vincent Rainardi
About Book-
In this book, you will learn how to build a data warehouse, including defining the architecture, understanding the methodology, gathering the requirements, designing the data models, and creating the databases.
This book is around SQL Server-based ETL processes and contains hundreds of practical, real-life scenarios. You will also learn how to present data to users using reports and multidimensional databases.
Where to Buy this Book?
You can buy this book on Amazon- Building a Data Warehouse: With Examples in SQL Server
7. Big Data: Principles and best practices of scalable real-time data systems
Author- Nathan Marz, James Warren
About Book-
This book provides the theory of big data systems and how to implement them in practice. And you will also learn specific technologies like Hadoop, Storm, and NoSQL databases.
You will understand the big data architecture and its general premise in a simplified way. This book covers all details of both theory and technical solutions for building real-time big data with Lambda Architecture.
Where to Buy this Book?
You can buy this book on Amazon- Big Data: Principles and best practices of scalable realtime data systems
Conclusion
In this article, you discovered the 7 Best Data Engineering Books. Have you Bought or Read any of these Books?. If yes, then tell your experience in the comment section.
I hope these 7 Best Data Engineering Books will help you to begin your Learning Journey.
All the Best!
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Though of the Day…
‘ It’s what you learn after you know it all that counts.’
– John Wooden
Written By Aqsa Zafar
Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.