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Learning Spark SQL

You're reading from   Learning Spark SQL Architect streaming analytics and machine learning solutions

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781785888359
Length 452 pages
Edition 1st Edition
Languages
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Author (1):
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Aurobindo Sarkar Aurobindo Sarkar
Author Profile Icon Aurobindo Sarkar
Aurobindo Sarkar
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Spark SQL FREE CHAPTER 2. Using Spark SQL for Processing Structured and Semistructured Data 3. Using Spark SQL for Data Exploration 4. Using Spark SQL for Data Munging 5. Using Spark SQL in Streaming Applications 6. Using Spark SQL in Machine Learning Applications 7. Using Spark SQL in Graph Applications 8. Using Spark SQL with SparkR 9. Developing Applications with Spark SQL 10. Using Spark SQL in Deep Learning Applications 11. Tuning Spark SQL Components for Performance 12. Spark SQL in Large-Scale Application Architectures

Implementing a Spark ML clustering model


In this section, we will explain with Spark ML. We will a publicly available Dataset about the student's knowledge status about a subject.

Note

The Dataset is available for download from the UCI website at https://archive.ics.uci.edu/ml/datasets/User+Knowledge+Modeling.

The attributes of the records contained in the Dataset have reproduced here from the UCI website mentioned previously for reference:

  • STG: The degree of study time for goal object materials (input value)
  • SCG: The degree of repetition number of users for goal object materials (input value)
  • STR: The degree of study time of users for related objects with the goal object (input value)
  • LPR: The exam performance of a user for related objects with the goal object (input value)
  • PEG: The exam performance of a user for goal objects (input value)
  • UNS: The knowledge level of the user (target value)

First, we will write a UDF to create two levels representing the two categories of the students--beneath...

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