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Frank Kane's Taming Big Data with Apache Spark and Python

You're reading from   Frank Kane's Taming Big Data with Apache Spark and Python Real-world examples to help you analyze large datasets with Apache Spark

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787287945
Length 296 pages
Edition 1st Edition
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Concepts
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (13) Chapters Close

Title Page
Credits
About the Author
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Spark FREE CHAPTER 2. Spark Basics and Spark Examples 3. Advanced Examples of Spark Programs 4. Running Spark on a Cluster 5. SparkSQL, DataFrames, and DataSets 6. Other Spark Technologies and Libraries 7. Where to Go From Here? – Learning More About Spark and Data Science

Running the similar-movies script using Spark's cluster manager


Leading up to this point has been a lot of work, but we now have a Spark program that should give us similar movies to each other. We can figure out what movies are similar to each other, just based on similarities between user ratings. Let's turn this movie similarities problem into some real code, run it, and look at the results. Go to the download package for this book, you will find a movie-similarities script. Download that to your SparkCourse folder and open it up. We're going to keep on using the MovieLens 100,000 rating dataset for this example, so there's no new data to download, just the script. This is the most complicated thing we're going to do in this course, so let's just get through the script and walk through what it's doing. We described it at a high level in the previous section, but let's go through it again.

Examining the script

You can see we're importing the usual stuff at the top of the script. We do need...

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