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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
Languages
Concepts
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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 2. Abstracting Data with RDDs FREE CHAPTER 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

Preparing the data


The example scenario we will use for the cookbook is on-time flight performance data (that is, flights scenario) that will make use of two sets of data:

  • Airline On-Time Performance and Causes of Flight Delays, available at http://bit.ly/2ccJPPM. These datasets contain information about scheduled and actual departure and arrival times of flights, and the delay causes. The data is represented as reported by US air carriers and is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).
  • OpenFlights, airport and airline data available at http://openflights.org/data.html. This dataset contains the list of US airport data, including the IATA code, airport name, and airport location.

We will create two DataFrames: one for the airports and one for the flights. The airports DataFrame will make up our vertices and the flights DataFrames will represent all the edges of our GraphFrame.

Getting ready

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