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

Congratulations on reaching the final chapter. In the last part of this book we will review the Big Data approaches presented earlier and will discuss the future of Big Data analytics using R . Whenever possible you will be provided with links and references to online and printed resources which you may use to expand your skills further in selected topics on Big Data with R. After reading this chapter you will be able to:

  • Summarize major Big Data technologies available on the market and explain how they can be integrated with the R language

  • Indicate the current position of R and its distributions in the landscape of statistical tools for Big Data analytics

  • Identify potential opportunities for future development of the R language and how it can become an integral part of Big Data workflows