Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (18 chapters)
Learning Apache Spark 2
Credits
About the Author
About the Reviewers
www.packtpub.com
Customer Feedback
Preface

Chapter 9. Building a Recommendation System

In the last chapter, we covered the concepts around deploying Spark across various clusters. Over the course of this and the next chapter we will look at some practical use cases. In this chapter, we will look at building a Recommendation System, which is what most of us are building in one way or another. We'll cover the following topics:

  • Overview of a recommendation system
  • Why do you need a recommendation system?
  • The long tail phenomenon
  • Types of Recommendations
  • Key problems in recommendations
  • Content-based recommendations
  • Collaborative filtering
  • Latent factor models

This chapter will hopefully give you a good introduction to recommender systems, and then follow up with specific code examples to solve a real world use case of movie recommendations.

Let's get started.