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Hands-On Data Analysis with Scala

You're reading from   Hands-On Data Analysis with Scala Perform data collection, processing, manipulation, and visualization with Scala

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789346114
Length 298 pages
Edition 1st Edition
Languages
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Author (1):
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Rajesh Gupta Rajesh Gupta
Author Profile Icon Rajesh Gupta
Rajesh Gupta
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Table of Contents (14) Chapters Close

Preface 1. Section 1: Scala and Data Analysis Life Cycle FREE CHAPTER
2. Scala Overview 3. Data Analysis Life Cycle 4. Data Ingestion 5. Data Exploration and Visualization 6. Applying Statistics and Hypothesis Testing 7. Section 2: Advanced Data Analysis and Machine Learning
8. Introduction to Spark for Distributed Data Analysis 9. Traditional Machine Learning for Data Analysis 10. Section 3: Real-Time Data Analysis and Scalability
11. Near Real-Time Data Analysis Using Streaming 12. Working with Data at Scale 13. Another Book You May Enjoy

Overview of streaming

Stream processing is the act of continuously computing results as new data becomes available. A very simple example of this is computing the average of some numbers in a continuous fashion. To begin with, we start with the following information:

  • Number of items = 0
  • Current average = 0

As a new number comes in, we perform the following steps:

  1. Compute a new total = Number of items x Current average + New number
  2. Increment the number of items by one
  3. Set the current average = New total / Number of items

As you can see, the continuous average computation algorithm is quite different from the batch-oriented algorithm. It is important to bear in mind the following facts when using this algorithm:

  • The average value gets updated as new numbers become available
  • The previously computed average value is reused to compute a new average

The following recipe using...

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