Hadoop: The Definitive Guide 4th Edition by Tom White shows comprehensive guide so you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.
You’ll find illuminating case studies that demonstrate how Hadoop is used to solve specific problems. This third edition covers recent changes to Hadoop, including material on the new MapReduce API, as well as MapReduce 2 and its more flexible execution model (YARN). You can store large datasets with the Hadoop Distributed File System (HDFS).
By reading this book, you can run distributed computations with MapReduce, use Hadoop’s data and I/O building blocks for compression, data integrity, serialization (including Avro), and persistence, discover common pitfalls and advanced features for writing real-world MapReduce programs, design, build, and administer a dedicated Hadoop cluster—or run Hadoop in the cloud.
There are also guidelines to load data from relational databases into HDFS, using Sqoop, perform large-scale data processing with the Pig query language, analyze datasets with Hive, Hadoop’s data warehousing system, take advantage of HBase for structured and semi-structured data, and ZooKeeper for building distributed systems.
This book is able to bring you from no knowledge about Hadoop and the Big Data to a full knowledge of Hadoop and its usage. The book is split in 16 chapters and 3 appendix for a total of 628 pages of contents. This make ~33 pages for chapter so it's easy to read and to find what you need. This is very important since – even if it's possible – it's rare that this kind of book is read cover-to-cover.
The author, Tom White, does not limit himself to Hadoop itself, but does help the reader to understand the Hadoop ecosystem and White speaks a lot about some products of the Hadoop ecosystem giving to each of them a full chapter. These products are: Pig, Hive, HBase, ZooKeeper and Sqoop. The last chapter is a "Case Studies" chapter. In this chapter is shown the usage of Hadoop in some situations you could have thought were difficult or impossible to handle for Hadoop.
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