We know that Hadoop Big Data processing with MapReduce is a big deal for statisticians, web analysts, and product managers who used to use the R tool for analyses because supplementary programming knowledge of MapReduce is required to migrate the analyses into MapReduce with Hadoop. Also, we know R is a tool that is consistently increasing in popularity; there are many packages/libraries that are being developed for integrating with R. So to develop a MapReduce algorithm or program that runs with the log of R and computation power of Hadoop, we require the middleware for R and Hadoop. RHadoop, RHIPE, and Hadoop streaming are the middleware that help develop and execute Hadoop MapReduce within R. In this last section, we will talk about RHadoop, RHIPE, and introducing Hadoop streaming, and from the later chapters we will purely develop MapReduce with these packages.
Big Data Analytics with R and Hadoop
By :
Big Data Analytics with R and Hadoop
By:
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
Free Chapter
Getting Ready to Use R and Hadoop
Writing Hadoop MapReduce Programs
Integrating R and Hadoop
Using Hadoop Streaming with R
Learning Data Analytics with R and Hadoop
Understanding Big Data Analysis with Machine Learning
Importing and Exporting Data from Various DBs
References
Index
Customer Reviews