Looking for the best Hadoop books? We have shortlisted the top 5 best Hadoop books that have been recommended by several Hadoop Developers and Architects as a must read for anybody willing to learn and practice Big Data and Hadoop. If you are not a big fan of reading and prefer online Hadoop trainings to help you learn Big Data and Hadoop faster, some of the below listed Big data and Hadoop books would serve as great reference.
We would love to review and add additional books to the list based on your feedback. If you have read other Big Data and Hadoop books that are worth adding to the list, mention it in the comments and we will review those Hadoop books and add them to the list, if they make the cut.
Best Hadoop Books – 2019
by Tom White
This is the best book for hadoop beginners. This is a best source to adapt you to the world of big data management. Hadoop: The Definitive Guide helps you harness the power of your data. Ideal for processing large datasets, the Apache Hadoop framework is an open source implementation of the MapReduce algorithm on which Google built its empire. This comprehensive resource demonstrates how to use Hadoop to build reliable, scalable, distributed systems: programmers will find details for analyzing large datasets, and administrators will learn how to set up and run Hadoop clusters. Complete with case studies that illustrate how Hadoop solves specific problems, this book helps you:Use the Hadoop Distributed File System (HDFS) for storing large datasets, and run distributed computations over those datasets using MapReduceBecome familiar with Hadoop’s data and I/O building blocks for compression, data integrity, serialization, and persistenceDiscover common pitfalls and advanced features for writing real-world MapReduce programsDesign, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloudUse Pig, a high-level query language for large-scale data processingTake advantage of HBase, Hadoop’s database for structured and semi-structured dataLearn ZooKeeper, a toolkit of coordination primitives for building distributed systems
Click the following link to buy this must read hadoop book – Hadoop, The Definitive Guide
by Alex Holmes
For developers working with big data, it’s not enough to have a theoretical understanding of Hadoop. They need to solve real challenges like analyzing real-time streams, moving data securely between storage systems, and managing large-scale clusters. The Hadoop ecosystem is constantly growing, and it’s important they keep up with the new technologies and practices to stay productive and future-proof data systems.
Hadoop in Practice, Second Edition provides over 100 tested, instantly-useful techniques that will help conquer big data, using Hadoop. This revised new edition covers changes and new features in the Hadoop core architecture, including MapReduce 2. Brand new chapters cover YARN, real-time use cases, and integrating Kafka, Storm, and Spark with Hadoop. There’s also a new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new versions recently. In short, this is the most practical, up-to-date coverage of Hadoop available anywhere.
Click the following link to buy this must read hadoop book for practical learning – Hadoop, In Practice
by Chuck Lam
A very good book to start your journey on Hadoop and MapReduce programming. It’s easy to read with excellent good examples. Big data can be difficult to handle using traditional data-bases. Apache Hadoop is a NoSQL applications framework that runs on distributed clusters. This lets it scale to huge datasets. If you need analytic information from your data, Hadoop’s the way togo. Hadoop in Action introduces the subject and teachers you how to write programs in the MapReduce style. It starts with a few easy examples and then moves quickly to show Hadoop use in more complex data analysis tasks. included are best practices and design patterns of MapReduce programming. This book requires basic Java skills. Knowing basic statistical concepts can help with the more advanced examples.
Click the following link to buy this must read Hadoop and MapReduce book – Hadoop in Action
by Boris Lublinsky, Kevin T. Smith, Alexey Yakubovich
The go-to guidebook for deploying Big Data solutions with Hadoop
Today’s enterprise architects need to understand how Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions. The Hadoop Solutions book is a practical, detailed guide to building and implementing those solutions, with code-level instruction in the popular Wrox tradition. It covers storing data with HDFS and Hbase, processing data with MapReduce, and automating data processing with Oozie. Hadoop security, running Hadoop with AWAS, best practices, and automating Hadoop processes in real time are also covered in depth.
With in-depth code examples in Java and XML and the latest on recent additions to the Hadoop ecosystem, this complete resource also covers the use of APIs, exposing their inner workings and allowing architects and developers to better leverage and customize them.The ultimate guide for developers, designers, and architects who need to build and deploy Hadoop applicationsCovers storing and processing data with various technologies, automating data processing, Hadoop security, and delivering real-time solutionsIncludes detailed, real-world examples and code-level guidelinesExplains when, why, and how to use these tools effectivelyWritten by a team of Hadoop experts in the programmer-to-programmer Wrox style
Click the following link to purchase this hadoop reference book that every Developer and Architects must read to maximize the power of Hadoop – Professional Hadoop Solutions.
by Nathan Marz and James Warren
This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful.
Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does complexity. Fortunately, scalability and simplicity are not mutually exclusive- rather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.
Big Data shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy to understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to use them in practice, and how to deploy and operate them once they’re built.
Click the following link to buy this must read hadoop book to understand the theory of big data systems, how to use them in practice, and how to deploy and operate them once they’re built – Big Data: Principles and and best practices of scalable realtime data systems.
Listed below are some of the online big data and hadoop training for you to get started as a Hadoop Developer or Hadoop Admin.
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