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Big Data Analytics with R and Hadoop (Community Experience Distilled) [Englisch] [Taschenbuch]

Vignesh Prajapati

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25. November 2013 Community Experience Distilled

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.


  • 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

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.

What you will learn from this book

  • 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


Big Data Analytics with R and Hadoop is a tutorial style book that focuses on all the powerful big data tasks that can be achieved by integrating R and Hadoop.

Who this book is written 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.

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Über den Autor und weitere Mitwirkende

Vignesh Prajapati

Vignesh Prajapati, from India, is a Big Data enthusiast, a Pingax ( consultant and a software professional at Enjay. He is an experienced ML Data engineer. He is experienced with Machine learning and Big Data technologies such as R, Hadoop, Mahout, Pig, Hive, and related Hadoop components to analyze datasets to achieve informative insights by data analytics cycles.

He pursued B.E from Gujarat Technological University in 2012 and started his career as Data Engineer at Tatvic. His professional experience includes working on the development of various Data analytics algorithms for Google Analytics data source, for providing economic value to the products. To get the ML in action, he implemented several analytical apps in collaboration with Google Analytics and Google Prediction API services. He also contributes to the R community by developing the RGoogleAnalytics' R library as an open source code Google project and writes articles on Data-driven technologies.

Vignesh is not limited to a single domain; he has also worked for developing various interactive apps via various Google APIs, such as Google Analytics API, Realtime API, Google Prediction API, Google Chart API, and Translate API with the Java and PHP platforms. He is highly interested in the development of open source technologies.

Vignesh has also reviewed the Apache Mahout Cookbook for Packt Publishing. This book provides a fresh, scope-oriented approach to the Mahout world for beginners as well as advanced users. Mahout Cookbook is specially designed to make users aware of the different possible machine learning applications, strategies, and algorithms to produce an intelligent as well as Big Data application.

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Buchdeckel | Copyright | Inhaltsverzeichnis | Auszug | Stichwortverzeichnis | Rückseite
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Die hilfreichsten Kundenrezensionen auf (beta) 3.1 von 5 Sternen  12 Rezensionen
16 von 17 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Doesn't cut it 15. Februar 2014
Von Dimitri Shvorob - Veröffentlicht auf
Format:Kindle Edition
Here I am, a beginner in need of guidance and happy to reward a helpful getting-started book with praise. Fast-forward one day - it's not gonna happen. Having installed Hadoop and almost installed RHadoop (oh boy, it is going to take a while before this technology becomes as user-friendly as any Microsoft product), I have found the book's directions regarding installation in-your-face inadequate - and once I started relying on online references (such as Michael Noll's Hadoop-on-Ubuntu article and resources linked from Revolution Analytics RHadoop Wiki), I really do not feel like going back to this poorly written and edited mess with perhaps 70 smallish pages of useful content. (RHIPE - not interested. Hadoop Streaming - not interested. Interfacing R with MySQL, etc. - does not belong in the book, and not interested). At $10, the book, despite its flaws, could be entertained as a convenience, but at $20 - not to mention $45 for a paper copy - it's "Forget about it, Packt".

PS. A sentence on page 43 catches my attention.

"The MapReduce framework is notoriously difficult to leverage for transformational logic that is not as simple, for example, real-time streaming, graph processing, and message passing".

The "notoriously difficult to leverage" bit sounds a bit too eloquent when compared to the surrounding text. A quick Google search, and hey-ho,

"The MapReduce framework is notoriously difficult to leverage for more than simple transformational logic".

says a 2012 white paper by ParAccel Inc. It figures.
8 von 8 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen worst excuse for a book I have ever tried to read 16. April 2014
Von Brian Boatright - Veröffentlicht auf
Format:Kindle Edition
I never write reviews, but the several hours I wasted trying to decipher this POS impels me to alert others to avoid this time sink. I have never seen in print such poorly crafted prose even on Wikipedia. The contents here we're obviously copy-pasted from various web sites and redundantly at that, by someone or some group of folks who have little fluency in English nor Hadoop, R, Big Data ...

Seriously I could rate a -1
5 von 5 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen not a good read 14. Februar 2014
Von Hari R - Veröffentlicht auf
I got a copy of this book and went through it and i am not impressed. The author has just said whats out there and does not seem to have the real experience of actually worked on these. Its more like a pointer work and i did not find any thing informative or deep learning of sort on concepts. Most info on the book can be got just by googling and reading .org websites intro page..
Would not recommend to buy this. 2 star for those simple examples. I felt extreme redundant material when i went from chapter to chapter. donot buy.
4 von 4 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen Its not Big Data Analytics 21. Februar 2014
Von deepak - Veröffentlicht auf
The book title says "Big Data Analytics with R and Hadoop" but when i read it it looked as if this is hands on book in RHadoop like how to install and go for it.
It missed some good real time examples around data analytics
3 von 3 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Really a bad surprise 1. Mai 2014
Von Giovanni d'Ario - Veröffentlicht auf
Format:Kindle Edition
I don't usually write angry reviews, and I am really sorry to do so now, but this book has too many issues to ignore them. I have purchased other books from Packt, and I consider it a serious and good quality company. This is why I really can't understand how they managed to publish something like this. The English is appalling and the content isn't any better. Explanations are superficial, unclear or chaotic. Mistakes and meaningless sentences pop up all the time. What's event worse is that, according to the publisher, this book has been reviewed by four (yes four!) different reviewers. It takes less then 30 minutes to understand that this book needs to be rewritten from scratch, so the natural question is: have these these gentlemen read this book? I seriously doubt it. It is really a missed opportunity, since this book would have filled a very important gap. I am really, really disappointed. Maybe, after massive rewriting and editing, it could become at least acceptable, but as for now, don't do the mistake I have done purchasing it.
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