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

Other programming abstractions


Hadoop is not just extended by additional functionality; there are tools to provide entirely different paradigms for writing the code used to process your data within Hadoop.

Pig

We mentioned Pig (http://pig.apache.org) in Chapter 8, A Relational View on Data with Hive, and won't say much else about it here. Just remember that it is available and may be useful if you have processes or people for whom a data flow definition of the Hadoop processes is a more intuitive or better fit than writing raw MapReduce code or HiveQL scripts. Remember that the major difference is that Pig is an imperative language (it defines how the process will be executed), while Hive is more declarative (defines the desired results but not how they will be produced).

Cascading

Cascading is not an Apache project but is open source and is available at http://www.cascading.org. While Hive and Pig effectively define different languages with which to express data processing, Cascading provides...