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PySpark Cookbook

You're reading from   PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788835367
Length 330 pages
Edition 1st Edition
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Authors (2):
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Tomasz Drabas Tomasz Drabas
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Tomasz Drabas
Denny Lee Denny Lee
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Denny Lee
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Toc

Table of Contents (9) Chapters Close

Preface 1. Installing and Configuring Spark FREE CHAPTER 2. Abstracting Data with RDDs 3. Abstracting Data with DataFrames 4. Preparing Data for Modeling 5. Machine Learning with MLlib 6. Machine Learning with the ML Module 7. Structured Streaming with PySpark 8. GraphFrames – Graph Theory with PySpark

Introduction

Now that we have a thorough understanding of how RDDs and DataFrames work and what they can do, we can start preparing ourselves and our data for modeling. 

Someone famous (Albert Einstein) once said (paraphrasing):

"The universe and the problems with any dataset are infinite, and I am not sure about the former."

The preceding is of course a joke. However, any dataset you work with, be it acquired at work, found online, collected yourself, or obtained through any other means, is dirty until proven otherwise; you should not trust it, you should not play with it, you should not even look at it until such time that you have proven to yourself that it is sufficiently clean (there is no such thing as totally clean).

What problems can your dataset have? Well, to name a few:

  • Duplicated observations: These arise through systemic and...
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