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From Apache Wiki <wikidi...@apache.org>
Subject [Hadoop Wiki] Update of "Books" by Jitesh Gawali
Date Mon, 11 Feb 2013 08:02:43 GMT
Dear Wiki user,

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The "Books" page has been changed by Jitesh Gawali:

  == Books in Print ==
  Here are the books that are currently in print -in order of publishing-, along with the
Hadoop version they were written against. One problem anyone writing a book will encounter
is that Hadoop is a very fast-moving target, and that things can change fast. Usually this
is for the better, when a book says "Hadoop can't" they really mean "the version of Hadoop
we worked with couldn't", and that the situation may have improved since then. If you have
any query about Hadoop, don't be afraid to ask on the relevant user mailing lists.
+ === Hadoop Real World Solutions Cookbook ===
+ '''Name:''' [[http://www.packtpub.com/hadoop-real-world-solutions-cookbook/book|Hadoop Real
World Solutions Cookbook]]
+ '''Author:''' Jonathan Owens, Brian Femiano, Jon Lentz
+ '''Hadoop Version:''' CDH3
+ '''Publisher:''' Packt Publishing
+ '''Date of Publishing:''' February 7, 2013
+ '''Sample Chapter:''' [[http://www.packtpub.com/sites/default/files/9781849519120_Chapter_06.pdf|Chapter
6: Big Data Analysis]]
+ Collection of real world code analytics and design patterns using various tools from the
Hadoop community. Each recipe walks the reader through the implementation, or in some cases
debugging and configuration tuning. The book covers various tools including !MapReduce, Hive,
Pig, MRUnit, serialization using Avro/Thrift/ProtoBuffs, Giraph, Accumulo and several others.

  === Hadoop MapReduce Cookbook ===
@@ -20, +37 @@

  '''Date of Publishing:''' January 25, 2013
- '''Sample Chapter:''' [[https://www.packtpub.com/sites/default/files/9781849517287_Chapter_06.pdf|Chapter
+ '''Sample Chapter:''' [[https://www.packtpub.com/sites/default/files/9781849517287_Chapter_06.pdf|Chapter
6: Analytics]]
  Hadoop !MapReduce Cookbook is a one-stop guide to processing large and complex data sets
using the Hadoop ecosystem. The book introduces you to simple examples and then dives deep
to solve in-depth big data use cases.

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