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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

Understanding and Tailing Data Streams

This chapter does a deep dive on the most critical aspect of the real-time application, which is about getting the streaming data from the source to the compute component. We will discuss the expectations and choices which are available. We will also walk the reader through which ones are more appropriate for certain use cases and scenarios. We will give high-level setup and some basic use cases for each of them. In this chapter, we will also introduce technologies related to data ingestion for the use cases.

The following is the list of components:

  • Understanding data streams
  • Setting up infrastructure for data ingestion
  • Taping data from the source to the processor: expectations and caveats
  • Comparing and choosing what works best for your use case
  • Do it yourself
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