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
Credits
About the Author
Acknowledgement
About the Reviewers
www.PacktPub.com
Preface

Summary


In this chapter we introduced you to the Apache Spark engine for fast Big Data processing. We explained how to launch a multi-node HDInsight cluster with Hadoop, Spark, and the Hive database installed and how to connect all these resources to RStudio Server.

We then used Bay Area Bike Share open data to guide you through the numerous functions of the SparkR package for data management, transformations, and analysis on data stored in Hive tables directly from the R console.

In Chapter 8, Machine Learning Methods for Big Data in R, we will explore another powerful dimension of Big Data analytics using R language: we will apply a variety of predictive analytics algorithms to large-scale data sets using the H2O platform for distributed machine learning of Big Data.