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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

Flink persistence


Flink provides a connector with the sinks or persistences, such as:

  • Apache Kafka
  • Elasticsearch
  • Hadoop Filesystem
  • RabbitMQ
  • Amazon Kinesis Streams
  • Apache NiFi
  • Apache Casssandra

In this book, we will discuss the Flink and Cassandra connection as it is the most popular.

Integration with Cassandra

We have discussed and explained the setup of Cassandra in previous chapters so we will directly go to the program required to make a connection between Flink and Cassandra:

  • Add dependencies in pom.xml:
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-cassandra_2.11</artifactId>
    <version>1.2.0</version>
</dependency>
<dependency>
    <groupId>com.codahale.metrics</groupId>
    <artifactId>metrics-json</artifactId>
    <version>3.0.2</version>
</dependency>
  • Create the data stream:
DataStream<Tuple4<Long,Integer,Integer,Long>> messageStream = env.addSource...
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