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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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Toc

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

Enriching

Data enrichment is the act of adding more information to raw data. Some examples of the enrichments are as follows:

  • Adding missing values
  • Adding lookup values
  • Joining with other datasets
  • Filtering
  • Aggregating

Continuing on the same example of using Person, let's say that the state element is optional. Given the zip information, we should be able to derive the value of state. In this specific case, we are performing the following two enrichments:

  • Looking up or deriving the value of state based on the zip value
  • Adding missing state value

Let's define a simple Scala function to map a US zip code to state. Please refer to Wikipedia (https://en.wikipedia.org/wiki/List_of_ZIP_code_prefixes) for more info on US ZIP codes:

  1. Define a Scala case class called Person with a method called cleanCopy to provide a clean copy of the object:
scala> case class Person...
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