Although Spark can be deployed in single-node, standalone mode, its powerful capabilities are best fit for multi-node applications. With this in mind, we will dedicate most of this chapter to practical Big Data crunching with Spark and R on a Microsoft Azure HDInsight cluster. As you should already be familiar with the deployment process of HDInsight clusters, our Spark workflows will contain one additional twist—€”the Spark framework will process the data straight from the **Hive** database, which will be populated with tables from HDFS. The introduction of Hive is a useful extension of the concepts covered in Chapter 5, *R with Relational Database Management Systems (RDBMSs)* and Chapter 6, *R with Non-Relational (NoSQL) Databases*, where we discussed the connectivity of R with relational and non-relational databases. But before we can use it, we should firstly launch a new HDInsight cluster with Spark and RStudio.

#### Big Data Analytics with R

##### By :

#### Big Data Analytics with R

##### By:

#### 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

Free Chapter

The Era of Big Data

Introduction to R Programming Language and Statistical Environment

Unleashing the Power of R from Within

Hadoop and MapReduce Framework for R

R with Relational Database Management Systems (RDBMSs)

R with Non-Relational (NoSQL) Databases

Faster than Hadoop - Spark with R

Machine Learning Methods for Big Data in R

The Future of R - Big, Fast, and Smart Data

Customer Reviews