Book Image

Big Data Analytics with Hadoop 3

By : Sridhar Alla
Book Image

Big Data Analytics with Hadoop 3

By: Sridhar Alla

Overview of this book

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Packt Upsell
Scientific Computing and Big Data Analysis with Python and Hadoop


This chapter is written to help current R users who are novices in Hadoop understand and select solutions to evaluate. As with most things open source, the first consideration is of course monetary. Isn't it always? The good news is that there are multiple alternatives that are free, and additional capabilities are under development in various open source projects.

We generally see four options for building R and Hadoop integration using entirely open source stacks:

  • Install R on workstations and connect to the data in Hadoop
  • Install R on a shared server and connect to Hadoop
  • Utilize Revolution R Open
  • Execute R inside of MapReduce using RMR2

Let's walk through each option in detail in the following sections.

Install R on workstations and connect to the data in Hadoop

This baseline approach's greatest advantage is simplicity and cost. It's free. End to end free. What else in life is? Through the packages Revolution contributed as open source, including rhdfs and rhbase, R users can directly...