Free Sample
+ Collection

Big Data Analytics with R and Hadoop

Vignesh Prajapati

If you’re an R developer looking to harness the power of big data analytics with Hadoop, then this book tells you everything you need to integrate the two. You’ll end up capable of building a data analytics engine with huge potential.
RRP $29.99
RRP $49.99
Print + eBook

Want this title & more?

$12.99 p/month

Subscribe to PacktLib

Enjoy full and instant access to over 2000 books and videos – you’ll find everything you need to stay ahead of the curve and make sure you can always get the job done.

Book Details

ISBN 139781782163282
Paperback238 pages

About This Book

  • Write Hadoop MapReduce within R
  • Learn data analytics with R and the Hadoop platform
  • Handle HDFS data within R
  • Understand Hadoop streaming with R
  • Encode and enrich datasets into R

Who This Book Is For

This book is ideal for R developers who are looking for a way to perform big data analytics with Hadoop. This book is also aimed at those who know Hadoop and want to build some intelligent applications over Big data with R packages. It would be helpful if readers have basic knowledge of R.

Table of Contents

Chapter 1: Getting Ready to Use R and Hadoop
Installing R
Installing RStudio
Understanding the features of R language
Installing Hadoop
Understanding Hadoop features
Learning the HDFS and MapReduce architecture
Understanding Hadoop subprojects
Chapter 2: Writing Hadoop MapReduce Programs
Understanding the basics of MapReduce
Introducing Hadoop MapReduce
Understanding the Hadoop MapReduce fundamentals
Writing a Hadoop MapReduce example
Learning the different ways to write Hadoop MapReduce in R
Chapter 3: Integrating R and Hadoop
Introducing RHIPE
Introducing RHadoop
Chapter 4: Using Hadoop Streaming with R
Understanding the basics of Hadoop streaming
Understanding how to run Hadoop streaming with R
Exploring the HadoopStreaming R package
Chapter 5: Learning Data Analytics with R and Hadoop
Understanding the data analytics project life cycle
Understanding data analytics problems
Chapter 6: Understanding Big Data Analysis with Machine Learning
Introduction to machine learning
Supervised machine-learning algorithms
Unsupervised machine learning algorithm
Recommendation algorithms
Chapter 7: Importing and Exporting Data from Various DBs
Learning about data files as database
Understanding MySQL
Understanding Excel
Understanding MongoDB
Understanding SQLite
Understanding PostgreSQL
Understanding Hive
Understanding HBase

What You Will Learn

  • Integrate R and Hadoop via RHIPE, RHadoop, and Hadoop streaming
  • Develop and run a MapReduce application that runs with R and Hadoop
  • Handle HDFS data from within R using RHIPE and RHadoop
  • Run Hadoop streaming and MapReduce with R
  • Import and export from various data sources to R

In Detail

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.

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.

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.


Read More