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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

Distinct advantages of Spark


Now that we understand the Spark components, let's move to the next step to understand what the key advantages of spark are for distributed, fault tolerant processing over its peers in this section. We will also touch upon the situations where Spark might not be the best choice for the solution:

  • High performance: This is the key feature responsible for the success of Spark, the high performance in data processing over HDFS. As we have seen in the previous section, Spark leverages its framework over HDFS and the Yarn eco-system, but offers up to 10x faster performance; this makes it a better choice over map-reduce. Spark achieves this performance enhancement by limiting the use of latency intensive disk I/O and leveraging over it in memory compute capability.
  • Robust and dynamic: Apache Spark is robust in its out-of-the-box implementation and it comes with over 80+ operations. It's built in Scala and has interfacing APIs in Java, Python, and so on. The entire combination...
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