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 have looked at Hive in this chapter and learned how it provides many tools and features that will be familiar to anyone who uses relational databases. Instead of requiring development of MapReduce applications, Hive makes the power of Hadoop available to a much broader community.

In particular, we downloaded and installed Hive, learning that it is a client application that translates its HiveQL language into MapReduce code, which it submits to a Hadoop cluster. We explored Hive's mechanism for creating tables and running queries against these tables. We saw how Hive can support various underlying data file formats and structures and how to modify those options.

We also appreciated that Hive tables are largely a logical construct and that behind the scenes, all the SQL-like operations on tables are in fact executed by MapReduce jobs on HDFS files. We then saw how Hive supports powerful features such as joins and views and how to partition our tables to aid in efficient query execution...