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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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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

Overview of Storm

Storm is an open source, distributed, resilient, real-time processing engine. It was started by Nathan Marz in late 2010. He was working at BackType. On his blog, he mentioned the challenges he faced while building Storm. It is a must read: http://nathanmarz.com/blog/history-of-apache-storm-and-lessons-learned.html.

Here is the crux of the whole blog: initially, real-time processing was implemented like pushing messages into a queue and then reading the messages from it using Python or any other language and processing them one by one. The challenges with this approach are:

  • In case of failure of the processing of any message, it has to be put back into the queue for reprocessing
  • Keeping queues and the worker (processing unit) up and running all the time

What follows are two sparking ideas by Nathan that make Storm capable of being a highly reliable and real...

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