Book Image

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

By : Frank Kane
Book Image

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

By: Frank Kane

Overview of this book

Frank Kane’s Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you’ll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease.
Table of Contents (13 chapters)
Title Page
Credits
About the Author
www.PacktPub.com
Customer Feedback
Preface
7
Where to Go From Here? – Learning More About Spark and Data Science

Item-based collaborative filtering in Spark, cache(), and persist()


We're now going to cover a topic that's near and dear to my heart-collaborative filtering. Have you ever been to some place like amazon.com and seen something like "people who bought this also bought," or have you seen "similar movies" suggested on imdb.com? I used to work on that. In this section, I'm going to show you some general algorithms on how that works under the hood. Now I can't tell you exactly how Amazon does it, because Jeff Bezos would hunt me down and probably do terrible things to me, but I can tell you some generally known techniques that you can build upon for doing something similar. Let's talk about a technique called item-based collaborative filtering and discuss how that works. We'll apply it to our MovieLens data to actually figure out similar movies to each other based on user ratings.

We're doing some pretty complicated and advanced stuff at this point in the book. The good news is this is probably...