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

Chapter 7. Faster than Hadoop - Spark with R

In Chapter 4, Hadoop and MapReduce Framework for R, you learned about Hadoop and MapReduce frameworks that enable users to process and analyze massive datasets stored in the Hadoop Distributed File System (HDFS). We launched a multi-node Hadoop cluster to run some heavy data crunching jobs using R language which would not be otherwise achievable on an average personal computer with any of the R distributions installed. We also said that although Hadoop is extremely powerful, it is generally recommended for data that greatly exceeds the memory limitations due to its rather slow processing. In this chapter we would like to present Apache Spark engine–a faster way to process and analyze Big Data. After reading this chapter, you should be able to:

  • Understand and appreciate Spark characteristics and functionalities

  • Deploy a fully-operational, multi-node Microsoft Azure HDInsight cluster with Hadoop, Spark, and Hive fully-configured and ready to use

  • Import...