Hadoop: The Definitive Guide: The Definitive Guide Review

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Hadoop: The Definitive Guide: The Definitive Guide ReviewThe second edition of the already fantastic Hadoop: The Definitive Guide adds the last few missing bits to the best Hadoop reference out there.
For those not familiar with the first edition, Hadoop: The Definitive Guide is exactly what it claims to be. If you're not already familiar with Hadoop, the first and second chapters (Meet Hadoop and MapReduce, respectively) take you through the basics in both concept as well as code. For those used to writing data processing applications, the rationale behind Hadoop and why it's useful are immediately apparent. If you've already been exposed to Hadoop, these chapters may be redundant but they're worth reading anyway the first time through.
The chapter on HDFS does a great job at explaining the underbelly of Hadoop's distributed file system including the Java APIs. The section on Hadoop IO is probably introduced a bit too early - Hadoop newbies probably don't care about compression and serialization prior to reading about map reduce - but excellent none the less in its detail. That said, you'll *really* want to go back and read it to understand the details of how compression codecs work after you learn more about map reduce.The "Writing a Map Reduce Application" chapter is probably the one existing users of Hadoop will skip. First timers will definitely get a lot out of a step by step walk through of a Java MR job from beginning to end.
The chapters on how map reduce works, types and formats (including input / output format details), and the advanced features (counters, sorting, the distributed cache, join libraries) are the ones you'll reread and reference constantly. The explanation, for instance, on how input splits are calculated demystifies the border between HDFS and the map reduce layer (and finally answers the question of "how does Hadoop know not to split in the middle of a record?"). Buy this book for these chapters, if not for the others.
The chapters on HBase, Pig, ZooKeeper, and Sqoop are excellent and, in some cases, the best reference on the topic to date.
There are enough corrections, updates, and new chapters that it's worth buying the second edition if you already have the first. For anyone new to Hadoop this is a must have. If you already use Hadoop the later chapters are what you're looking for; a deep explanation of not just "how," but "why."
Some reviewers have noted the discussion of deprecated APIs. This really isn't a flaw of the book, but of premature deprecation within Hadoop itself. The newer APIs didn't have all the features of the old and anyone writing production map reduce jobs would wind up needing a lot of those features. I think the author does a great job with a tough situation while still alerting the reader that newer APIs are on the horizon. Besides, the differences are so few that it's almost not worth mentioning. While APIs may change, the core design, execution model, and architecture of Hadoop haven't changed and this is the best book on the subject.Hadoop: The Definitive Guide: The Definitive Guide Overview
Discover how Apache Hadoop can unleash the power of your data. This comprehensive resource shows you how to build and maintain reliable, scalable, distributed systems with the Hadoop framework -- an open source implementation of MapReduce, the algorithm on which Google built its empire. Programmers will find details for analyzing datasets of any size, and administrators will learn how to set up and run Hadoop clusters.

This revised edition covers recent changes to Hadoop, including new features such as Hive, Sqoop, and Avro. It also provides illuminating case studies that illustrate how Hadoop is used to solve specific problems. Looking to get the most out of your data? This is your book.

Use the Hadoop Distributed File System (HDFS) for storing large datasets, then run distributed computations over those datasets with MapReduce
Become familiar with Hadoop's data and I/O building blocks for compression, data integrity, serialization, 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
Use Pig, a high-level query language for large-scale data processing
Analyze datasets with Hive, Hadoop's data warehousing system
Take advantage of HBase, Hadoop's database for structured and semi-structured data
Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems

"Now you have the opportunity to learn about Hadoop from a master -- not only of the technology, but also of common sense and plain talk."--Doug Cutting, Cloudera


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