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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

Spark pragmatic concepts

You know what appeals the most to us developers? The ability to tap into the framework and the flexibility to extend it as per our needs. In today's world of abstraction and decoupling, this is taken care of using a variety of APIs that come out of the box.

We have talked enough about the latency issue the big data world was struggling with before Spark came and took the performance to the next level. Let's have a closer look to understand this latency problem a little better. The following diagram captures the execution of typical Hadoop processes and its intermediate steps:

Well, as depicted, Hadoop ecosystem leverages HDFS (a disk-based distributed stable storage) extensively to store the intermediate processing results:

Job #1: This reads the data for processing from HDFS and writes its results to HDFS
Job #2: This reads the interim processing results of job 1 from HDFS, processes, and writes the outcome to HDFS

While HDFS is a fault tolerant and persistent store...

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