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

Spark for Big Data analytics

Spark is often considered as a new, faster, and more advanced engine for Big Data analytics that could soon overthrow Hadoop as the most widely used Big Data tool. In fact, there is already a visible trend for many businesses to opt for Spark rather than Hadoop in their daily data processing activities. Undoubtedly, Spark has several selling points that make it a more attractive alternative to the slightly complicated, and sometimes clunky Hadoop:

  • It's pretty fast and can reduce the processing time by up to 100 times when run in memory as compared to standard Hadoop MapReduce jobs or up to 10 times if run on disk.

  • It's a very flexible tool that can run as a standalone application, but also can be deployed on top of Hadoop and HDFS, and other distributed file systems.

  • It can use a variety of data sources from standard relational databases, through HBase, Hive, to Amazon S3 containers. It may also be launched in the cloud. In fact, in the tutorial in this chapter...