Must-Read Data Engineering Books in 2024
Data engineering is a fast evolving area that is critical to the success of data-driven businesses. To flourish in their professions, data engineers must stay current on the latest technologies, principles, and best practices.
The environment of data engineering continues to evolve in 2024, and professionals must investigate a variety of tools to keep up with industry trends. In this blog article, we’ll look at five must-read data engineering books that will provide you with the information and abilities you need to succeed in this dynamic area.
Learning Spark by Holden Karau
Title: Learning Spark, Author: Holden Karau
“Learning Spark” by Holden Karau remains a must-read for anyone exploring big data and distributed computing with Apache Spark.
With Spark growing more popular for large-scale data processing, this book dives deep into the framework’s key concepts. Karau covers everything from RDDs (Resilient Distributed Datasets) to Spark SQL and machine learning with MLlib in a beginner-friendly yet thorough manner.
Learning Spark is more than a book; it is a step-by-step guide to understanding one of the most influential technologies in the data engineering toolbox.
Read here: “https://amzn.to/4aDrwMh”
Big Data: Principles and best practices of scalable realtime data systems by Nathan Marz
Title: Big Data: Principles and Best Practices of Scalable Real-Time Data Systems Author: Nathan Marz
The book by Nathan Marz is a timeless classic that delves into the foundations and best practices for developing scalable and real-time data systems. Marz digs into the core ideas of batch processing and stream processing, bringing insights on developing systems that can manage both historical and real-time data, with a focus on the Lambda Architecture.
Marz’s book is a must-read for those aiming to construct strong and scalable systems as real-time data processing gains importance in data engineering.
Read here: “https://amzn.to/3H4OpLc”
Big Data, Black Book: Covers Hadoop 2, MapReduce, Hive, YARN, Pig, R, and Data Visualization
This comprehensive black book by multiple writers is a gold mine for data engineers looking for a thorough understanding of various big data technologies. The book presents a comprehensive picture of the big data ecosystem, covering Hadoop 2, MapReduce, Hive, YARN, Pig, R, and data visualization tools.
It provides as a practical guide for establishing big data solutions, with practical examples and case studies. The Big Data Black Book is an important guide for navigating the complexities of big data technologies, whether you are a beginner or an established professional.
Read here : “https://amzn.to/3H4OpLc”
The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball
Title: The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling Author: Ralph Kimball
“The Data Warehouse Toolkit” by Ralph Kimball is a standard reference for data engineers working in the design and construction of data warehouses. Dimensional modeling is a vital component of building effective data warehouses, and Kimball’s book gives a complete reference to dimensional modeling principles and best practices.
This book provides data engineers with the expertise they need to develop efficient and scalable data warehouses, from creating star and snowflake schemas to navigating slowly changing dimensions.
Read here: “https://amzn.to/3S1c0m1”
DW 2.0 — The Architecture for the Next Generation of Data Warehousing by The Father of Data Warehousing W.H. Inmon
Title: DW 2.0 — The Architecture for the Next Generation of Data Warehousing Author: W.H. Inmon
W.H. Inmon, often referred to as the “Father of Data Warehousing,” presents his vision for the next generation of data warehousing in “DW 2.0.”
Inmon investigates the evolution of data warehousing and proposes the concept of DW 2.0, an architecture built to satisfy the demands of modern data environments, in this book. Inmon’s book, which covers issues such as unstructured data, real-time processing, and data governance, presents a forward-looking perspective on the future of data warehousing, making it important reading for data engineers creating tomorrow’s data architecture.
Read here: “https://amzn.to/3S2XCKg”
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