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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 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

Creating RDDs

For this recipe, we will start creating an RDD by generating the data within the PySpark. To create RDDs in Apache Spark, you will need to first install Spark as shown in the previous chapter. You can use the PySpark shell and/or Jupyter notebook to run these code samples.

Getting ready 

We require a working installation of Spark. This means that you would have followed the steps outlined in the previous chapter. As a reminder, to start PySpark shell for your local Spark cluster, you can run this command:

./bin/pyspark --master local[n]

Where n is the number of cores. 

How to do it...

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You have been reading a chapter from
PySpark Cookbook
Published in: Jun 2018
Publisher: Packt
ISBN-13: 9781788835367
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