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Big Data Analytics with Hadoop 3

You're reading from   Big Data Analytics with Hadoop 3 Build highly effective analytics solutions to gain valuable insight into your big data

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Length 482 pages
Edition 1st Edition
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Author (1):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Hadoop FREE CHAPTER 2. Overview of Big Data Analytics 3. Big Data Processing with MapReduce 4. Scientific Computing and Big Data Analysis with Python and Hadoop 5. Statistical Big Data Computing with R and Hadoop 6. Batch Analytics with Apache Spark 7. Real-Time Analytics with Apache Spark 8. Batch Analytics with Apache Flink 9. Stream Processing with Apache Flink 10. Visualizing Big Data 11. Introduction to Cloud Computing 12. Using Amazon Web Services

Introduction

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...

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