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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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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (8) Chapters Close

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

Finding the most popular movie


Let's start by reviewing the data format of the MovieLens dataset, the u.data file.

As you might recall, the u.data file on each line, consists of a user ID, a movie ID, a rating, and a timestamp. Each line says, "this user watched this movie, gave it this rating, and did it at this time":

Our task is to just figure out which movie was watched most often or which movie ID appears most frequently in the entire dataset. This isn't a very hard thing to do; in fact, if you want to go give it a crack yourself, feel free. In this section we'll take a look at the implementation that I came up with, get that to run, and see what we come up with

Examining the popular-movies script

In the download package for this book, you'll find a popular-movies Python script. Download that, put it in your SparkCourse folder, and open it up. We will print out a list of all of the movies and the number of times they appear, and then sort them based on how often they appear. It's a pretty...

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