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

Relational Database Management Systems (RDBMSs)

The abundance of  RDBMSs currently available means that it's nearly impossible to describe all or at least a large majority of them in one single chapter. If you haven't worked with any such databases in your analytical or research career, now is the best time to explore how they can benefit your Big Data processing and management activities.

A short overview of used RDBMSs

In order to give you a taste of the variety of databases available to R users, we decided to present three of them, which can be launched and connected from R in three different scenarios:

  • Locally on a personal computer

  • Locally on a virtual machine

  • Remotely with a database on a server and RStudio installed on a personal local machine

Our selection criteria also included the requirements that all databases are open-source or at least free to use, are well-maintained with an active community of users, and can operate on multiple platforms (at least on Mac OS X, Windows, and Linux...