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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

Flink architecture and execution engine

Flink is a platform for distributed stream and batch data processing. The core of Flink is a streaming dataflow engine. Flink is based on Kappa architecture. Kappa architecture was introduced in 2014 by Jay Kreps in https://www.oreilly.com/ideas/questioning-the-lambda-architecture which addresses the pitfalls of Lambda architecture. So before going into the details of Flink, let's figure out the base of Flink: Kappa architecture. Kappa architecture is designed to handle real-time data processing and continuous data reprocessing using a single data stream. There were two main concerns raised related to Lambda architecture: maintaining two different code bases for real-time analytics and batch analytics on the same source data and the reprocessing of events will require code changes which is not easy to maintain as seen in the following figure:

So in Kappa architecture, there is everything in streaming data. In case of the failure and reprocessing of...

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