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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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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

Storm architecture and its components

We have discussed enough about the history and theory of abstractions of Storm; it's now time to dive in and see the framework in execution and get hands on to the real code to actually see Storm in action. We are just one step away from the action part. Before we get there, let's understand what are the various components that get to play in Storm and what is their contribution in the building and orchestration of this framework.

Storm execution can be done in two flavors:

  • Local mode: This is a single node and a nondistributed setup that is generally used for demo and testing. Here, the entire topology is executed in a single worker and thus a single JVM.
  • Distributed mode: This is a multinode setup that is fully or partially distributed and this is the recommended mode for real-time application development and deployment.

The instructions can be referred to from the Apache Storm site at https://storm.apache.org/documentation/Setting-up-a-Storm...

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