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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

Running the case study


Before running the code, let's build it using the following command:

mvn clean install

This will create a JAR file with the name as chapter13-0.0.1-SNAPSHOT-jar-with-dependencies.jar in chapter13/target directory.

Load Hazelcast

Execute the following command to load static values into Hazelcast:

java -cp target/chapter12-0.0.1-SNAPSHOT-jar-with-dependencies.jar com.book.simulator.HazelCastLoader <phone_number> vehicle-static-data 10000

The output will be as the following screenshot. Wait for the next step to be executed as the program reads only the latest entries from the Kafka topic:

Hazelcast UI will display one map: vehicleAlertInfo, as shown in the following screenshot:

Generate Vehicle static value

Execute the following command to generate vehicle static values which contains the vehicle and its starting location as latitude and longitude. It pushes into Kafka:

java -cp target/chapter12-0.0.1-SNAPSHOT-jar-with-dependencies.jar com.book.simulator.VehicleStartPointGenerator...
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