Book Image

Big Data Analytics with R and Hadoop

By : Vignesh Prajapati
Book Image

Big Data Analytics with R and Hadoop

By: Vignesh Prajapati

Overview of this book

<p>Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue. New methods of working with big data, such as Hadoop and MapReduce, offer alternatives to traditional data warehousing. <br /><br />Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and RHadoop. A powerful data analytics engine can be built, which can process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics operations of R, MapReduce, and HDFS of Hadoop.<br /><br />You will start with the installation and configuration of R and Hadoop. Next, you will discover information on various practical data analytics examples with R and Hadoop. Finally, you will learn how to import/export from various data sources to R. Big Data Analytics with R and Hadoop will also give you an easy understanding of the R and Hadoop connectors RHIPE, RHadoop, and Hadoop streaming.</p>
Table of Contents (16 chapters)
Big Data Analytics with R and Hadoop
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Preface
Index

Introducing RHIPE


RHIPE stands for R and Hadoop Integrated Programming Environment. As mentioned on http://www.datadr.org/, it means "in a moment" in Greek and is a merger of R and Hadoop. It was first developed by Saptarshi Guha for his PhD thesis in the Department of Statistics at Purdue University in 2012. Currently this is carried out by the Department of Statistics team at Purdue University and other active Google discussion groups.

The RHIPE package uses the Divide and Recombine technique to perform data analytics over Big Data. In this technique, data is divided into subsets, computation is performed over those subsets by specific R analytics operations, and the output is combined. RHIPE has mainly been designed to accomplish two goals that are as follows:

  • Allowing you to perform in-depth analysis of large as well as small data.

  • Allowing users to perform the analytics operations within R using a lower-level language. RHIPE is designed with several functions that help perform Hadoop...