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Practical Real-time Data Processing and Analytics

You're reading from Practical Real-time Data Processing and Analytics

Product type Book

Published in Sep 2017

Publisher Packt

ISBN-13 9781787281202

Pages 360 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (20) Chapters

Title Page

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Introducing Real-Time Analytics

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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