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

Chapter 5. Statistical Big Data Computing with R and Hadoop

This chapter provides an introduction to R and how to use R to perform statistical computing on big data using Hadoop. We will see alternatives ranging from open source R on workstations to parallelized commercial products such as Revolution R Enterprise, and many other options in between will present themselves. Between these extremes lie a range of options with unique abilities: scaling data, performance, capability, and ease of use. And so, the right choice or choices depend on your data size, budget, skill, patience, and governance limitations.

In this chapter, we will summarize the alternatives and some of their advantages using pure open source R. Also, we will describe the options for achieving even greater scale, speed, stability, and ease of development by combining open source and commercial technologies.

In a nutshell, the following topics will be covered in this chapter:

  • Introduction to integrating R with Hadoop
  • Methods...