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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

Summary


In this chapter, we explained all the possible sinks available with Storm. There is in-built integration available between Storm and the sinks but they are not mature enough to run on the required configuration. So in this chapter we used plain Java code to connect with any external tool for linkage. First we explained the integration of Storm and the latest version of Cassandra. Then we looked at in-memory databases which are required in all types of usecases related to Storm. We explained the integration of Storm and Hazelcast. After integration with Cassandra and Hazelcast, one more important integration was explained: the presentation layer. So we chose Elasticsearch with Grafana, and completed the example. In the end, we provided one problem to the reader so that the reader can think about and write the code using the same sinks that were explained in the initial part of the chapter.

 

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