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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

DIY

Lot of examples are shown in the previous sections. Now, let's get ready to do some hands-on. Code will be available in the code bundle for reference. Read README.MD in the code bundle for the execution of the program.

Source to sink – Flink execution:

We have already seen examples where we integrated Apache Kafka or RabbitMQ as source to Cassandra as sink. Now, here we will integrate Apache Kafka as source and ElasticSearch 2.x as sink. The connector will be changed. Instead of using Cassandra, we will use ElasticSearch.

Here we have the import files:

package com.boof.flink.diy;
import java.net.InetAddress;
import java.net.InetSocketAddress;
import java.util.ArrayList;import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import org.apache.flink.api.common.functions.RuntimeContext;
import org.apache.flink.streaming.api.datastream...
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