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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

Do It Yourself


Here we will string Storm, Kafka, Hazelcast, and Cassandra together and build a use case. This use case is based on telecoms data which is uniquely identified using phone numbers. Telecoms real-time packet data is entered into Kafka. The system has to store the total usage (bytes) per phone number into Hazelcast and persist the total usage into Cassandra and also persist each event into Cassandra.

Pseudo code:

  • Create CassandraBolt which persists data in Cassandra.
  • Create a bolt which reads values from Hazelcast on the basis of phone numbers and adds up with the current value. Also update the same entry back in Hazelcast.
  • Create a topology to link the Kafka spout to the custom bolt mentioned in the previous step and then CassandraBolt to persist the total usage. Also link Kafka spout to CassandraBolt to persist each event.

Insert the code from the bundle:

package com.book.chapter7.diy; 
 
Here we have the import files 
 
import java.util.Date; 
import java.util.Properties; 
import...
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