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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
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Shilpi Saxena
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Table of Contents (17) Chapters Close

Real-Time Big Data Analytics
Credits
About the Authors
About the Reviewer
www.PacktPub.com
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 persistence


Now that we know Storm and its internals very well, let's wire in persistence to Storm. Well we have done all the computation and code, so now it's very important to store the computed results or intermediate references into a database or some persistence store. You have the choice of writing your own JDBC bolts or you can actually use the implementation provided for using Storm persistence.

Let's start with writing our own JDBC persistence first. Once we have touched upon the nitty gritty, then we can look at and appreciate what Storm provides for. Let's say we are setting up software system at toll gates that could monitor the emission rates of vehicles and track the details of the vehicles that are running at emissions beyond the prescribed limit.

Here, all the vehicle details and their emissions are captured in the file that is being read by the file reader spout. The spout then reads the record and feeds it into the topology where it is consumed by the parser bolt, which...

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