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Hadoop Real-World Solutions Cookbook- Second Edition

You're reading from   Hadoop Real-World Solutions Cookbook- Second Edition Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784395506
Length 290 pages
Edition 2nd Edition
Tools
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Author (1):
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Tanmay Deshpande Tanmay Deshpande
Author Profile Icon Tanmay Deshpande
Tanmay Deshpande
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Table of Contents (12) Chapters Close

Preface 1. Getting Started with Hadoop 2.X FREE CHAPTER 2. Exploring HDFS 3. Mastering Map Reduce Programs 4. Data Analysis Using Hive, Pig, and Hbase 5. Advanced Data Analysis Using Hive 6. Data Import/Export Using Sqoop and Flume 7. Automation of Hadoop Tasks Using Oozie 8. Machine Learning and Predictive Analytics Using Mahout and R 9. Integration with Apache Spark 10. Hadoop Use Cases Index

Introduction


Hadoop consists of two important components: HDFS and Map Reduce. In the previous chapter, we talked about various recipes one can perform to use and maintain HDFS in a good state. In this chapter, we are going to explore the details of the Map Reduce framework. Hadoop itself is written in Java, and Java is, of course, a preferred way to write Map Reduce programs, but this does not restrict you to only using Java. It provides libraries, such as Hadoop-Streaming, Hadoop Pipes, and so on so that you can write map reduce programs in most popular languages. These include C++, C#, Shell Scripting using Hadoop-Streaming, and so on.

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