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 Hadoop streaming


Hadoop streaming is a Hadoop utility for running the Hadoop MapReduce job with executable scripts such as Mapper and Reducer. This is similar to the pipe operation in Linux. With this, the text input file is printed on stream (stdin), which is provided as an input to Mapper and the output (stdout) of Mapper is provided as an input to Reducer; finally, Reducer writes the output to the HDFS directory.

The main advantage of the Hadoop streaming utility is that it allows Java as well as non-Java programmed MapReduce jobs to be executed over Hadoop clusters. Also, it takes care of the progress of running MapReduce jobs. The Hadoop streaming supports the Perl, Python, PHP, R, and C++ programming languages. To run an application written in other programming languages, the developer just needs to translate the application logic into the Mapper and Reducer sections with the key and value output elements. We learned in Chapter 2, Writing Hadoop MapReduce...