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©Manning Publications Co. Please post comments or corrections to the Author Online forum:
http://www.manning-sandbox.com/forum.jspa?forumID=544
TABLE OF CONTENTS
PART1 Hadoop - A Distributed Programming Framework
CHAPTER 1 Introducing Hadoop
CHAPTER 2 Starting Hadoop
CHAPTER 3 Components of Hadoop
PART 2 - Hadoop in Action
CHAPTER 4 Writing basic MapReduce programs
CHAPTER 5 Advanced MapReduce
CHAPTER 6 Programming practices
CHAPTER 7 Cookbook
CHAPTER 8 Managing Hadoop
PART 3 - Hadoop Gone Wild
CHAPTER 9 Running Hadoop in the cloud
CHAPTER 10 Programming with Pig
CHAPTER 11 Hive and the Hadoop herd
CHAPTER 12 Case studies
APPENDIX HDFS file commands
©Manning Publications Co. Please post comments or corrections to the Author Online forum:
http://www.manning-sandbox.com/forum.jspa?forumID=544
Part 1
Hadoop–A Distributed
Programming Framework
Part 1 of this book introduces the basics for understanding and using Hadoop.
We describe the hardware components that make up a Hadoop cluster, as well
as the installation and confi guration to create a working system. We cover the
MapReduce framework at a high level and get your fi rst MapReduce program up
and running.
©Manning Publications Co. Please post comments or corrections to the Author Online forum:
http://www.manning-sandbox.com/forum.jspa?forumID=544
1
3
Introducing Hadoop
This chapter covers
The basics of writing a scalable,
■
distributed data-intensive program
Understanding Hadoop and MapReduce
■
Writing and running a basic MapReduce program
■
Today, we’re surrounded by data. People upload videos, take pictures on their
cell phones, text friends, update their Facebook status, leave comments around
the web, click on ads, and so forth. Machines, too, are generating and keeping
more and more data. You may even be reading this book as digital data on your
computer screen, and certainly your purchase of this book is recorded as data with
some retailer.
1
The exponential growth of data fi rst presented challenges to cutting-edge
businesses such as Google, Yahoo, Amazon, and Microsoft. They needed to go
through terabytes and petabytes of data to fi gure out which websites were popular,
what books were in demand, and what kinds of ads appealed to people. Existing
tools were becoming inadequate to process such large data sets. Google was the fi rst
to publicize MapReduce—a system they had used to scale their data processing needs.
1
Of course, you’re reading a legitimate copy of this, right?
©Manning Publications Co. Please post comments or corrections to the Author Online forum:
http://www.manning-sandbox.com/forum.jspa?forumID=544
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