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

Chapter 7. Importing and Exporting Data from Various DBs

In this final chapter, we are going to see how data from different sources can be loaded into R for performing the data analytics operations. Here, we have considered some of the popular databases that are being used as data storage, required for performing data analytics with different applications and technologies. As we know, performing the analytics operations with R is quite easy as compared to the other analytics tools and again, it's free and open source. Since, R has available methods to use customized functions via installing R packages, many database packages are available in CRAN to perform database connection with R. Therefore, the R programming language is becoming more and more popular due to database, as well as operating system, independence.

We have specially designed this chapter to share knowledge of how data from various database systems can be loaded and used into R for performing data modeling. In this chapter...