Hadoop: The Definitive Guide at Dominant Systems Book Store

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 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
Take advantage of HBase, Hadoop's database for structured and semi-structured data
Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems

If you have lots of data -- whether it's gigabytes or petabytes -- Hadoop is the perfect solution. Hadoop: The Definitive Guide is the most thorough book available on the subject.

"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, Hadoop Founder, Yahoo!

Customer Reviews & Comments
These days, one can't seem to attend technical conferences without hearing marketing-oriented speakers' world domination plans for their products. So imagine this: what if pigs and elephants are involved? Elephants would be Hadoop installations, and Pigs would be one of those animal-themed tools, smarter cousins of the elephants really, riding on top of Hadoops, directing them on how to perform their jobs. Would the world be a better place? Hadoop is the brainchild of Doug Cutting, who named his creation after his kid's stuffed yellow elephant. Hadoop enables large datasets distributed over a cluster of machines to be processed in parallel. One machine or node in that cluster would usually house a JobTracker and a NameNode. The JobTracker schedules and manages processing jobs to be executed in the other machines, and the NameNode manages the metadata (e.g., file names and locations, etc) of the datasets to be processed. The processing jobs are programmed in the form of Map and Reduce functions. Inputs are usually split into blocks to be processed in parallel by two or more identical mappers. The close to final outputs are then fed to one or more identical reducers, whose job is to perform any final transformations on the intermediate data to produce data summaries in the expected format. Several companies are using Hadoop to extract knowledge from their extensive data. I've read this book and Jason Venners' Pro Hadoop book. Although I like both, I like this book better for the following reasons: more comprehensive coverage of topics, and more insiders' information on design rationales and how certain Hadoop features really work behind the scenes. Here's a breakdown of and some commentaries on the book's contents: Chapter One introduces Hadoop, its history and how it's different from similar tools or frameworks. Kinda dry. Chapter Two introduces the MapReduce Programming model and its benefits when compared to, say, the use of Unix tools for achieving parallel processing of text files. This is also where readers are introduced to the concepts of: map, combiner, and reduce functions, shuffle and sort, streaming, etc. Chapters Three and Four are all about the Hadoop Distributed FileSystems and I/O and the design decisions that were made to address performance, reliability, and safety concerns. Chapter Five shows you how to develop, configure, test, run and tune a MapReduce Application. Good chapter but Jason Venner's book has better materials on testing and debugging MapReduce applications. Chapters Six through Eight discuss how MapReduce really works behind the scene, including advanced features. This is where you'll learn how flexible Hadoop is when it comes to handling different types of inputs and outputs in terms of numbers, sizes, formats, and usage scenarios. Excellent! Chapters Nine and Ten are really good. They teach you how to set up and administer Hadoop clusters. There's even a brief but informative section on how to use Hadoop with Amazon EC2 servers. Chapters 11-13 devote one chapter each on how to install and interact with frameworks built on top of Hadoop: Pig, HBase, and ZooKeeper. Chapter 14 provides Case Studies (e.g., How Facebook uses Hadoop to analyze ad campaign effectiveness, etc.). Appendices A and B provide instructions on how to install Apache's Hadoop and Cloudera's distribution, respectively, and C gives you a runthrough of the steps to take when preparing to use the NCDC Weather Data used in the book. Very thorough and well written book. 4.5 stars rating.

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