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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 input sources


Storm works well with a variety of input data sources. Consider the following examples:

  • Kafka

  • RabbitMQ

  • Kinesis

Storm is actually a consumer and process of the data. It has to be coupled with some data source. Most of the time, data sources are connected devices that generate streaming data, for example:

  • Sensor data

  • Traffic signal data

  • Data from stock exchanges

  • Data from production lines

The list can be virtually endless and so would be the use cases that can be served with the Storm-based solutions. But in the essence of designing cohesive but low coupling systems, it's very important that we keep the source and computation lightly coupled. It's highly advisable that we use a queue or broker service to integrate the streaming data source with Storm's computation unit. The following diagram quickly captures the basic flow for any Storm-based streaming application, where the data is collated from the source and ingested into Storm:

The data is consumed, parsed, processed, and dumped...

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