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PySpark Cookbook

You're reading from PySpark Cookbook

Product type Book

Published in Jun 2018

Publisher Packt

ISBN-13 9781788835367

Pages 330 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Authors (2):

Denny Lee

Tomasz Drabas

View More author details

Table of Contents (13) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Installing and Configuring Spark

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

Index

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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Authors (2)

Denny Lee

Denny Lee is a Principal Program Manager at Microsoft for the Azure DocumentDB teamMicrosoft's blazing fast, planet-scale managed document store service. He is a hands-on distributed systems and data science engineer with more than 18 years of experience developing Internet-scale infrastructure, data platforms, and predictive analytics systems for both on-premise and cloud environments. He has extensive experience of building greenfield teams as well as turnaround/ change catalyst. Prior to joining the Azure DocumentDB team, Denny worked as a Technology Evangelist at Databricks; he has been working with Apache Spark since 0.5. He was also the Senior Director of Data Sciences Engineering at Concur, and was on the incubation team that built Microsoft's Hadoop on Windows and Azure service (currently known as HDInsight). Denny also has a Masters in Biomedical Informatics from Oregon Health and Sciences University and has architected and implemented powerful data solutions for enterprise healthcare customers for the last 15 years.

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Tomasz Drabas

Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 12 years' international experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting. Tomasz started his career in 2003 with LOT Polish Airlines in Warsaw, Poland while finishing his Master's degree in strategy management. In 2007, he moved to Sydney to pursue a doctoral degree in operations research at the University of New South Wales, School of Aviation; his research crossed boundaries between discrete choice modeling and airline operations research. During his time in Sydney, he worked as a Data Analyst for Beyond Analysis Australia and as a Senior Data Analyst/Data Scientist for Vodafone Hutchison Australia among others. He has also published scientific papers, attended international conferences, and served as a reviewer for scientific journals. In 2015 he relocated to Seattle to begin his work for Microsoft. While there, he has worked on numerous projects involving solving problems in high-dimensional feature space.

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