Book Image

Mastering Spark for Data Science

By : Andrew Morgan, Antoine Amend, Matthew Hallett, David George
Book Image

Mastering Spark for Data Science

By: Andrew Morgan, Antoine Amend, Matthew Hallett, David George

Overview of this book

Data science seeks to transform the world using data, and this is typically achieved through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs. This book deep dives into using Spark to deliver production-grade data science solutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more. You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.
Table of Contents (22 chapters)
Mastering Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Story mutation


We now have enough material to enter the heart of the subject. We were able to detect near-duplicate events and group similar articles within a story. In this section, we will be working in real time (on a Spark Streaming context), listening for news articles, grouping them into stories, but also looking at how these stories may change over time. We appreciate that the number of stories is undefined as we do not know in advance what events may arise in the coming days. As optimizing KMeans for each batch interval (15 mn in GDELT) would not be ideal, neither would it be efficient, we decided to take this constraint not as a limiting factor but really as an advantage in the detection of breaking news articles.

The Equilibrium state

If we were to divide the world's news articles into say 10 or 15 clusters, and fix that number to never change over time, then training a KMeans clustering should probably group similar (but not necessarily duplicate) articles into generic stories....