Book Image

Big Data Analytics with R

By : Simon Walkowiak
Book Image

Big Data Analytics with R

By: Simon Walkowiak

Overview of this book

Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.
Table of Contents (16 chapters)
Big Data Analytics with R
About the Author
About the Reviewers

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 ( 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.


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 time-consuming...