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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

Storm and IMDB integration for dimensional data


IMDB stands for In-Memory Database. An IMDB is required to keep intermediate results while processing the streaming of events or to keep static information related to events, which is not provided in events, for example. Employee details can be stored in IMDB on the basis of employee IDs and events that are coming in and out of an office. In this case, an event does not contain complete information about employees to save the network costs and for better performance, Therefore, when Storm processes the event, it will take static information regarding the employee from the IMDB and persist it along with the event details in Cassandra or any other database for further analytics. There are numerous open source IMDB tools available on the market, but some famous ones are Hazelcast, Memcached, and Redis.

Let's see how to integrate Storm and Hazelcast. No special setup is required for Hazelcast. Perform the following steps:

  1. Add the dependencies:
&lt...
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