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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 Cassandra topology


As discussed in Chapter 4, Setting up the Infrastructure for Storm, Storm has spouts and bolts. The Casandra bolt is required to persist records. There are two common ways to integrate Storm with Cassandra. The first is by using the storm-cassandra built-in library where you just need to call CassndraBolt and the required parameters. The second way is by using the DataStax Cassandra library, which needs to be imported using the build manager and using the wrapper classes to make a connection with Cassandra. The following are the steps to integrate Storm with Cassandra using the DataStax library:

  1. Add the following dependencies:
<dependency> 
    <groupId>com.datastax.cassandra</groupId> 
    <artifactId>cassandra-driver-core</artifactId> 
    <version>3.1.0</version> 
</dependency> 
<dependency> 
    <groupId>com.datastax.cassandra</groupId> 
    <artifactId>cassandra-driver-mapping</artifactId...
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