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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 overview

Apache Spark is a highly distributed compute engine, which comes with promises of speed and reliability for the computations. As a framework it's based on Hadoop, but it's further enhanced to perform in memory computations to cater to interactive queries and near real-time stream processing. The parallel processing clustering and in-memory processing offer Spark an edge in terms of performance and reliability. Today Apache Spark is known for its proven salient features:

Speed and efficiency: While it runs off traditional disk-based HDFS, it has 100x higher speed, because of in-memory computations and savings on disk I/O. It saves the intermediate results in memory, thus saving the overall execution time.
Extensibility and compatibility: It has a variety of interaction APIs for developers to choose from. It comes out of the box with Java, Scala, and Python APIs.
Analytics and ML: It provides robust support for all machine learning and graph algorithms. In fact, now it's becoming...

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