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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

Understanding data streams


A data stream is the continuous flow of any type of data using any medium. Out of 4 Vs of big data, two are velocity and variety. A data stream refers to both velocity and variety of data. Data stream is real-time data coming from sources such as social media sites or different monitoring sensors installed in manufacturing units or vehicles. Another example of streaming data processing is IOT, that is the Internet Of Things, where data is coming from different components though the internet.

Real-time data stream processing

There are two different kinds of streaming data: bounded and unbounded streams, as shown in the following images. Bounded streams have a defined start and a defined end of the data stream. Data processing stops once the end of the stream is reached. Generally, this is called batch processing. An unbounded stream does not have an end and data processing starts from the beginning. This is called real-time processing, which keeps the states of events...

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