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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

Summary

In this chapter, we explained what a data stream is and gave related examples, as well as looking at the real-time use cases related to data streams. We got readers acquainted and introduced setup and quick execution for different real-time data ingestion tools like Flume, NiFi, Logstash, and Fluentd. We also explained where these data ingestion tools stand in terms of reliability and scalability. Then, we tried to compare the data ingestion tools so that the reader could pick the tools as per the need for their use case, after comparing pros and cons. They can run the examples by running the code bundled in JAR easily on standalone as well as in cluster mode. In the end, we gave the reader a real-time problem to solve using data ingestion tools along with pseudo code, so that we could focus on coding the example rather than finding right solution.

As we are now aware of different types of data streaming tools, in the next chapter we will focus on setting up Storm. Storm is an open...

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