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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
Author Profile Icon Tomasz Drabas
Tomasz Drabas
Denny Lee Denny Lee
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Denny Lee
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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

Overview of RDD actions

As noted in preceding sections, there are two types of Apache Spark RDD operations: transformations and actions. An action returns a value to the driver after running a computation on the dataset, typically on the workers. In the preceding recipes, the take() and count() RDD operations are examples of actions.

Getting ready

This recipe will be reading a tab-delimited (or comma-delimited) file, so please ensure that you have a text (or CSV) file available. For your convenience, you can download the airport-codes-na.txt and departuredelays.csv files from learning http://bit.ly/2nroHbh. Ensure your local Spark cluster can access this file (~/data/flights/airport...

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