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Big Data Analytics with R

You're reading from   Big Data Analytics with R Leverage R Programming to uncover hidden patterns in your Big Data

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781786466457
Length 506 pages
Edition 1st Edition
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Author (1):
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Simon Walkowiak Simon Walkowiak
Author Profile Icon Simon Walkowiak
Simon Walkowiak
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Table of Contents (10) Chapters Close

Parallel R

In this part of of the chapter , we will introduce you to the concept of parallelism in R. More precisely, we will focus here almost entirely on explicit methods for parallel computation, in which users are capable of controlling the parallelization on a single machine. In Online Chapter, Pushing R Further ( https://www.packtpub.com/sites/default/files/downloads/5396_6457OS_PushingRFurther.pdf) you will practice some of these methods on much larger clusters of commodity hardware through popular cloud computing platforms such as Amazon EC2 or Microsoft Azure. In Chapter 4, Hadoop and MapReduce Framework for R you will learn much more about the MapReduce approach in R (through the HadoopStreaming, Rhipe, and RHadoop packages)-an abstraction of parallelism for distributed files systems such as Hadoop.

Note

Our motivation for parallel computing in R comes from the simple fact that many data-processing operations tend to be very similar, and some of them are extremely...

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