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 Hadoop MapReduce


Basically, the MapReduce model can be implemented in several languages, but apart from that, Hadoop MapReduce is a popular Java framework for easily written applications. It processes vast amounts of data (multiterabyte datasets) in parallel on large clusters (thousands of nodes) of commodity hardware in a reliable and fault-tolerant manner. This MapReduce paradigm is divided into two phases, Map and Reduce, that mainly deal with key-value pairs of data. The Map and Reduce tasks run sequentially in a cluster, and the output of the Map phase becomes the input of the Reduce phase.

All data input elements in MapReduce cannot be updated. If the input (key, value) pairs for mapping tasks are changed, it will not be reflected in the input files. The Mapper output will be piped to the appropriate Reducer grouped with the key attribute as input. This sequential data process will be carried away in a parallel manner with the help of Hadoop MapReduce algorithms as well...