Book Image

Hadoop Beginner's Guide

Book Image

Hadoop Beginner's Guide

Overview of this book

Data is arriving faster than you can process it and the overall volumes keep growing at a rate that keeps you awake at night. Hadoop can help you tame the data beast. Effective use of Hadoop however requires a mixture of programming, design, and system administration skills."Hadoop Beginner's Guide" removes the mystery from Hadoop, presenting Hadoop and related technologies with a focus on building working systems and getting the job done, using cloud services to do so when it makes sense. From basic concepts and initial setup through developing applications and keeping the system running as the data grows, the book gives the understanding needed to effectively use Hadoop to solve real world problems.Starting with the basics of installing and configuring Hadoop, the book explains how to develop applications, maintain the system, and how to use additional products to integrate with other systems.While learning different ways to develop applications to run on Hadoop the book also covers tools such as Hive, Sqoop, and Flume that show how Hadoop can be integrated with relational databases and log collection.In addition to examples on Hadoop clusters on Ubuntu uses of cloud services such as Amazon, EC2 and Elastic MapReduce are covered.
Table of Contents (19 chapters)
Hadoop Beginner's Guide
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Summary


We covered a lot of ground in this chapter, in regards to getting a Hadoop cluster up and running and executing MapReduce programs on it.

Specifically, we covered the prerequisites for running Hadoop on local Ubuntu hosts. We also saw how to install and configure a local Hadoop cluster in either standalone or pseudo-distributed modes. Then, we looked at how to access the HDFS filesystem and submit MapReduce jobs. We then moved on and learned what accounts are needed to access Elastic MapReduce and other AWS services.

We saw how to browse and create S3 buckets and objects using the AWS management console, and also how to create a job flow and use it to execute a MapReduce job on an EMR-hosted Hadoop cluster. We also discussed other ways of accessing AWS services and studied the differences between local and EMR-hosted Hadoop.

Now that we have learned about running Hadoop locally or on EMR, we are ready to start writing our own MapReduce programs, which is the topic of the next chapter...