Top 7 Reference Books for Hadoop Developers

The latest exploration of the new trends in technology and the dramatic impact it has on the economy, science and the society as a whole is the “Big Data” Technology. It refers to the ability to crunch vast information, instantly analyse it, and draw a pro-founding conclusion. Big Data technology has revolutionised the way people do business. Today, Big Data technology is the greatest buzzword in the tech industry, and every individual is seeking to make a paradigm career shift in this emerging and trending technology in Apache Hadoop.

Herein is our recommendation for some of the best books to learn Hadoop. Some of the books are for beginners while some are for MapReduce programmers, let’s roll our sleeves

1 .   The Definitive Guide by Tom White

The Definitive Guide is a comprehensive resource that demonstrates how to use Hadoop to build a    scalable, reliable and distributed system. Additionally, it provides details on how to analyze large sets of data which is ideal for programmers. On the other hand, administrators can get to learn how to setup and run Hadoop clusters.

Complete with illustrations and case studies on how Hadoop solves specific problems. Also, it helps in knowing how to:

♦ Use Hadoop Distributed File System to cache large data sets.
♦ Run distributed computations on specific data sets using MapReduce.
♦ Serialize, integrate, Compress and discover common mistakes and advanced features for building and writing real-world MapReduce program designs.

As a developer, The Definitive Guide helps you in harnessing the power of your data. However, beginners may find it hard to understand but to scan through the pages and with reading discipline, you will get most about in this book.

You can find more information about the book  @

2 .    MapReduce Design Patterns (Building Effective Algorithms & Analytics for Hadoop) by Donald Miner & Adam Shook

The book presumes the reader has a basic knowledge of Hadoop. Additionally, the guide brings together a unique collection of most valuable MapReduce patterns that can save you effort and time regardless of the development framework, language or domain you are using.

Succinctly, each pattern is explicitly explained. From caveats to pitfalls, each step is identified to help readers avoid common design mistakes when modeling their big data architecture. Also, it provides a complete review of MapReduce.

The book is best suited for advanced users. It contains various insightful methods on how to solve many Hadoop problems quickly. The concepts are summarized with interesting examples for instance in:

♦ Filtering patterns
♦ Data organization patterns
♦ Input and output patterns

This book is indispensable for anyone engrossed in Hadoop. You can find more information about the book  @

3.   Hadoop in Practice by Alex Holmes

Probably, this is the best book for practice in Hadoop. It collects nearly over 85 example and presents them in a problem-solution format. Each technique proffered addresses a particular task that you will face for instance writing a log file or querying a big data using Pig. 

The examples are presented in a step-wise format, learning on how to build and deploy specific solutions. Also, it introduces you to different methods you can use to integrate MapReduce and R. The author has used simple language making it the ideal book highly recommended for beginners.

You can find more information about the book  @

4.   Hadoop in Action by Chuck Lam

Hadoop in Action introduces readers on how to write MapReduce and Hadoop programs. Progressively, the book introduces programming and Hadoop terminologies in MapReduce. Gradually, its shows worked out examples to illustrate how Hadoop is used in complex data analysis task.

Also, it covers the best design and practices patterns of MapReduce programming. Both in implementation and conceptualization, MapReduce is a complex idea.

However, this book takes you far beyond the mechanics of running Hadoop to writing outstanding programs in MapReduce Framework. Notably, it assumes user familiarity with Java. Most of the examples presented are written in Java. Basic knowledge in statistical concepts will help the reader appreciate advanced data processing examples.

You can find more information about the book  @

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5.   Programming Pig by Gates Alan & Daniel Doi

This is the most outstanding book when learning Apache Pig a Hadoop ecosystem component that is used in processing data using Pig Latin scripts. The book provides basic and advanced level knowledge to readers on Pig including Grunt shell, and user defined functions for extending Pig language and the Pig Latin script language.

As well, you will learn how Pig converts the scripts to MapReduce programs for an effective working in Hadoop. You can find more information about the book  @

6.   Professional Hadoop Solutions by Boris Lublinksy, Kevin Smith, and Alexey Yakubovich

Professional Hadoop Solutions covers storing data with Hadoop Distributed File System and HBase, automating data processing with Oozie and MapReduce. As well, it includes Hadoop running on AWS (Amazon Web Services), Hadoop security, automating Hadoop process in real time and best practices.

With an in-depth code example in XML, Java and the recent addition to the Hadoop ecosystem, this complete guide also highlights the use of APIs, by allowing developers to leverage and customize the architects to suit their needs.

You can find more information about the book  @

7.   Programming Hive by Dean Wampler, Edward Capriolo & Jason Rutherglen

Do you want to move your relational database application to Hadoop? This comprehensive coverage introduces you to Apache Hive, the Hadoop warehouse infrastructure.

Through the guide, you will quickly learn on how to use Hives Structured query language (SQL) dialect-to query, analyze, and summarize large sets of datasets stored in Hadoop’s distributed File system.

Moreover, the guide is example driven as it shows developers how to setup and configure Hive your environment. It provides a detailed overview on MapReduce and Hadoop by demonstrating how Hive works within the Hadoop ecosystem.

Also, you will find real world case studies describing how companies have used Hive to resolve unique problems involving petabytes of data. You can find more information about the book  @

About Aditi Malhotra

Aditi Malhotra is the Content Marketing Manager at Whizlabs. Having a Master in Journalism and Mass Communication, she helps businesses stop playing around with Content Marketing and start seeing tangible ROI. A writer by day and a reader by night, she is a fine blend of both reality and fantasy. Apart from her professional commitments, she is also endearing to publish a book authored by her very soon.

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