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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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Toc

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

What this book covers

Section – A:  Introduction – Getting Familiar

This section gives the readers basic familiarity with the real-time analytics spectra and domains. We talk about the basic components and their building blocks. This sections consist of the following chapters:

  • Chapter 1: Introducing Real-Time Analytics
  • Chapter 2: Real-Time Application – The Basic Ingredients

Section – B:  Setup and Infrastructure

This section is predominantly setup-oriented, where we have the basic components set up. This sections consist of the following chapters:

  • Chapter 3: Understanding and Tailing Data Streams
  • Chapter 4: Setting Up the infrastructure for Storm
  • Chapter 5: Configuring Apache Spark and Flink

Section – C: Storm Computations

This section predominantly focuses on exploring Storm, its compute capabilities, and its various features. This sections consist of the following chapters:

  • Chapter 6: Integration of Source with Storm
  • Chapter 7: From Storm to Sink
  • Chapter 8: Storm Trident

Section – D: Using Spark Compute In Real Time

This section predominantly focuses on exploring Spark, its compute capabilities, and its various features. This sections consist of the following chapters:

  • Chapter 9: Working with Spark
  • Chapter 10: Working with Spark Operations
  • Chapter 11: Spark Streaming

Section – E: Flink for Real-Time Analytics
This section focuses on exploring Flink, its compute capabilities, and its various features.

  • Chapter 12: Working with Apache Flink

Section – F: Let’s Have It Stringed Together

This sections consist of the following chapters:

  • Chapter 13: Case Study 1
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