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 Hadoop MapReduce fundamentals


To understand Hadoop MapReduce fundamentals properly, we will:

  • Understand MapReduce objects

  • Learn how to decide the number of Maps in MapReduce

  • Learn how to decide the number of Reduces in MapReduce

  • Understand MapReduce dataflow

  • Take a closer look at Hadoop MapReduce terminologies

Understanding MapReduce objects

As we know, MapReduce operations in Hadoop are carried out mainly by three objects: Mapper, Reducer, and Driver.

  • Mapper: This is designed for the Map phase of MapReduce, which starts MapReduce operations by carrying input files and splitting them into several pieces. For each piece, it will emit a key-value data pair as the output value.

  • Reducer: This is designed for the Reduce phase of a MapReduce job; it accepts key-based grouped data from the Mapper output, reduces it by aggregation logic, and emits the (key, value) pair for the group of values.

  • Driver: This is the main file that drives the MapReduce process. It starts the execution of MapReduce...