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Practical Real-time Data Processing and Analytics

You're reading from   Practical Real-time Data Processing and Analytics Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Length 360 pages
Edition 1st Edition
Languages
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Authors (2):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
Saurabh Gupta Saurabh Gupta
Author Profile Icon Saurabh Gupta
Saurabh Gupta
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Table of Contents (14) Chapters Close

Preface 1. Introducing Real-Time Analytics FREE CHAPTER 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 introduced the readers to Apache Spark compute engine, and we talked about the various components of Spark framework and its schedulers. We touched upon the advantages that provide edge to spark as a scalable, high performing compute engine. The users were also acquainted with the not-so sparkling features of Spark—when it's not the best fit to be used. We walked the users through some practical realtime industry wide use cases. Next we got into the layered architecture of Spark and its internal working across the cluster. In the end we touched upon the pragmatic concepts of Spark: RDD, DataFrame, and datasets.

The next chapter will touch upon the spark APIs and execution of all components through working code blocks.

 

 

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