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Real-Time Big Data Analytics

You're reading from   Real-Time Big Data Analytics Design, process, and analyze large sets of complex data in real time

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Product type Paperback
Published in Feb 2016
Publisher
ISBN-13 9781784391409
Length 326 pages
Edition 1st Edition
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Author (1):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
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Toc

Table of Contents (12) Chapters Close

Preface 1. Introducing the Big Data Technology Landscape and Analytics Platform FREE CHAPTER 2. Getting Acquainted with Storm 3. Processing Data with Storm 4. Introduction to Trident and Optimizing Storm Performance 5. Getting Acquainted with Kinesis 6. Getting Acquainted with Spark 7. Programming with RDDs 8. SQL Query Engine for Spark – Spark SQL 9. Analysis of Streaming Data Using Spark Streaming 10. Introducing Lambda Architecture Index

How and when to use Storm

I am a believer of the fact that the quickest way to get acquainted to a tool or technology is to do it, and we have been doing a lot of theoretical talking so far rather than actually doing it, so let's actually begin the fun. We would start with the basic word count topology, I have lot of experience of using Storm on Linux, and there is a lot of online material available for the same. I have used a Windows VM for execution of the word count topology. Here are a couple of prerequisites:

  • apache-storm-0.9.5-src.
  • JDK 1.6+.
  • Python 2.x. (I figured this out by a little trial and error. My Ubuntu always had Python and it never gave any trouble; for example, the word count uses a Python script for splitting sentences, so I set up Python 3 (the latest version), but later figured out that the compatible one is 2.x.)
  • Maven.
  • Eclipse.

Here we go.

Set up the following environment variables accurately:

  • JAVA_HOME
  • MVN_HOME
  • PATH

The PATH variable should have the path to Python installation...

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