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Learning Hadoop 2

You're reading from Learning Hadoop 2 Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2

Product type Paperback

Published in Feb 2015

Publisher Packt

ISBN-13 9781783285518

Length 382 pages

Edition 1st Edition

Tools

Hadoop

Concepts

Data Processing

Table of Contents (13) Chapters

Preface

1. Introduction FREE CHAPTER

2. Storage

3. Processing – MapReduce and Beyond

4. Real-time Computation with Samza

5. Iterative Computation with Spark

6. Data Analysis with Apache Pig

7. Hadoop and SQL

8. Data Lifecycle Management

9. Making Development Easier

10. Running a Hadoop Cluster

11. Where to Go Next

Index

Chapter 2. Storage

After the overview of Hadoop in the previous chapter, we will now start looking at its various component parts in more detail. We will start at the conceptual bottom of the stack in this chapter: the means and mechanisms for storing data within Hadoop. In particular, we will discuss the following topics:

Describe the architecture of the Hadoop Distributed File System (HDFS)
Show what enhancements to HDFS have been made in Hadoop 2
Explore how to access HDFS using command-line tools and the Java API
Give a brief description of ZooKeeper—another (sort of) filesystem within Hadoop
Survey considerations for storing data in Hadoop and the available file formats

In Chapter 3, Processing – MapReduce and Beyond, we will describe how Hadoop provides the framework to allow data to be processed.

The rest of the chapter is locked

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