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Practical Real-time Data Processing and Analytics

You're reading from   Practical Real-time Data Processing and Analytics Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Length 360 pages
Edition 1st Edition
Languages
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Authors (2):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
Saurabh Gupta Saurabh Gupta
Author Profile Icon Saurabh Gupta
Saurabh Gupta
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introducing Real-Time Analytics FREE CHAPTER 2. Real Time Applications – The Basic Ingredients 3. Understanding and Tailing Data Streams 4. Setting up the Infrastructure for Storm 5. Configuring Apache Spark and Flink 6. Integrating Storm with a Data Source 7. From Storm to Sink 8. Storm Trident 9. Working with Spark 10. Working with Spark Operations 11. Spark Streaming 12. Working with Apache Flink 13. Case Study

Integrating Storm with a Data Source

This is the chapter where we integrate the source of data with Storm distributed compute engine. It involves stringing together the source of streaming data to a broker service like RabbitMQ and then wiring the streaming pipeline to Storm. We have a very interesting sensor data recipe here, which streams live data from a free real-time sensor data channel and pushes that into RabbitMQ and further to a Storm topology for business analysis .

  • RabbitMQ – messaging that works
  • RabbitMQ Exchanges
  • RabbitMQ Setup
  • RabbitMQ – publish and subscribe
  • RabbitMQ – integration with Storm
  • PubNub data stream publisher
  • Sensor data processing topology
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