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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

Product type Book
Published in Jun 2017
Publisher Packt
ISBN-13 9781787287945
Pages 296 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Frank Kane Frank Kane
Profile icon Frank Kane
LinkedIn
Frank Kane has spent nine years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers all the time. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaches others about big data analysis.
See other products by Frank Kane

Table of Contents (13) Chapters

Title Page
Credits
About the Author
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Spark 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

Creating similar movies from one million ratings - part 1

Let's modify our movie-similarities script to actually work on the 1 million ratings dataset and make it so it can run in the cloud on Amazon Elastic MapReduce, or any Spark cluster for that matter. So, if you haven't already, go grab the movie-similarities-1m Python script from the download package for this book, and save it wherever you want to. It's actually not that important where you save this one because we're not going to run it on your desktop anyway, you just need to look at it and know where it is. Open it up, just so we can take a peek, and I'll walk you through the stuff that we actually changed:

Changes to the script

Now, first of all, we made some changes so that it uses the 1 million ratings dataset from Grouplens instead of the 100,000 ratings dataset. If you want to grab that, go over to grouplens.org and click on datasets:

You'll find it in the MovieLens 1M Dataset:

This data is a little bit more current, it's from...

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Authors (1)

Profile icon Frank Kane
LinkedIn
Frank Kane has spent nine years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers all the time. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaches others about big data analysis.
See other products by Frank Kane

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