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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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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

Overview of RDD transformations

As noted in preceding sections, there are two types of operation that can be used to shape data in an RDD: transformations and actions. A transformation, as the name suggests, transforms one RDD into another. In other words, it takes an existing RDD and transforms it into one or more output RDDs. In the preceding recipes, we had used a map() function, which is an example of a transformation to split the data by its tab-delimiter.

Transformations are lazy (unlike actions). They only get executed when an action is called on an RDD. For example, calling the count() function is an action; more information is available in the following section on actions.

Getting ready

This recipe...

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PySpark Cookbook
Published in: Jun 2018
Publisher: Packt
ISBN-13: 9781788835367
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