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

Understanding the basics of MapReduce


Understanding the basics of MapReduce could well be a long-term solution if one doesn't have a cluster or uses Message Passing Interface (MPI). However, a more realistic use case is when the data doesn't fit on one disk but fits on a Distributed File System (DFS), or already lives on Hadoop-related software.

Moreover, MapReduce is a programming model that works in a distributed fashion, but it is not the only one that does. It might be illuminating to describe other programming models, for example, MPI and Bulk Synchronous Parallel (BSP). To process Big Data with tools such as R and several machine learning techniques requires a high-configuration machine, but that's not the permanent solution. So, distributed processing is the key to handling this data. This distributed computation can be implemented with the MapReduce programming model.

MapReduce is the one that answers the Big Data question. Logically, to process data we need parallel processing, which...