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Hadoop Beginner's Guide

Hadoop Beginner's Guide

3.7 (13)
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Hadoop Beginner's Guide

Hadoop Beginner's Guide

3.7 (13)

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)
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Hadoop Beginner's Guide
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Overview of Hive


Hive is a data warehouse that uses MapReduce to analyze data stored on HDFS. In particular, it provides a query language called HiveQL that closely resembles the common Structured Query Language (SQL) standard.

Why use Hive?

In Chapter 4, Developing MapReduce Programs, we introduced Hadoop Streaming and explained that one large benefit of Streaming is how it allows faster turn-around in the development of MapReduce jobs. Hive takes this a step further. Instead of providing a way of more quickly developing map and reduce tasks, it offers a query language based on the industry standard SQL. Hive takes these HiveQL statements and immediately and automatically translates the queries into one or more MapReduce jobs. It then executes the overall MapReduce program and returns the results to the user. Whereas Hadoop Streaming reduces the required code/compile/submit cycle, Hive removes it entirely and instead only requires the composition of HiveQL statements.

This interface to Hadoop...

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