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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

Other sources for input to Storm


In our earlier example, we have seen integrated with Storm, one of them being Kafka (just discussed in the previous section). In Storm samples, the word count topology (which we covered in detail in an earlier chapter) doesn't use any data source for input. Instead, some sentences are hardcoded in the spout itself and this seed data is emitted to the topology. This may be fine for test cases and samples, but for real-world implementations this is neither ideal nor expected. Storm has to feed a stream of live events into the topology in almost all of the real-world implementations. We can have a variety of input sources that can be integrated with Storm. Let's have a closer look at code snippets on what all we can plug in with Storm to feed the data.

A file as an input source

We can use a Storm spout to be effectively reading from a file; though that's not a real use case for a streaming app, but we can have Storm very well read in from the file. All we need...

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