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

Hadoop architecture

Apache Hadoop is an open source, integrated framework for Big Data processing and management, which can be relatively easy to deploy on commodity hardware. Hadoop can also be defined as an ecosystem of tools and methods that allow distributed storage and analytics of massive amounts of structured and unstructured data. In this section, we will present an array of tools, frameworks, and applications that come as integral parts of the Hadoop ecosystem and are responsible for a variety of data management and processing purposes.

Hadoop Distributed File System

As explained in Chapter 1, The Era of Big Data,  Hadoop Distributed File System (HDFS) derives from the original Google File System presented in 2003 in a paper titled The Google file system authored by Ghemawat, Gobioff, and Leung. The architecture and design of current HDFS (based on the Apache Hadoop 2.7.2 release) are explained thoroughly in the HDFS Architecture Guide available at the Apache website at http://hadoop...