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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

Packaging structure of Spark Streaming

In this section, we will discuss the various APIs and operations exposed by Spark Streaming.

Spark Streaming APIs

All Spark Streaming classes are packaged in the org.apache.spark.streaming.* package. Spark Streaming defines two core classes which also provide access to all Spark Streaming functionality, such as StreamingContext.scala and DStream.scala.

Let's examine the following functions and roles performed by these classes:

  • org.apache.spark.streaming.StreamingContext: This is an entry point to Spark Streaming functionality. It defines methods for creating the objects of DStream.scala and also for starting and stopping the Spark Streaming jobs.
  • org.apache.spark.streaming.dstream...
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