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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

Spark Streaming - introduction and architecture

Spark Streaming is a very useful extension to the Spark core API that's being widely used to process incoming streaming data in real-time or close to real-time as in near real-time (NRT). This API extension has all the core Spark features in terms of highly distributed, scalable, fault tolerant, and high throughput, low latency processing.

The following diagram captures how Spark Streaming works in close conjunction with the Spark execution engine to process real-time data streams:

Spark Streaming works on microbatching based architecture --we can envision it as an extension to the core Spark architecture where the framework performs real-time processing by actually clubbing the incoming events from the stream into deterministic batches. Each batch is of the same size, and the live data is collected and stacked into these...

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