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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

Taping data from source to the processor - expectations and caveats

In this section, we will discuss the expectations of log streaming tools in terms of performance, reliability, and scalability. The reliability of the system can be identified by message delivery semantics. There are three types of delivery semantics:

  • At most once: Messages are immediately transferred. If the transfer succeeds, the message is never sent out again. However, many failure scenarios can cause lost messages.
  • At least once: Each message is delivered at least once. In failure cases, messages may be delivered twice.
  • Exactly once: Each message is delivered once and only once.

Performance consists of I/O, CPU, and RAM usage and impact. By definition, scalability is the capability of a system, network, or process to handle a growing amount of work, or its potential to be enlarged in order to accommodate...

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