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
About the Authors
About the Reviewer
Customer Feedback

Chapter 10. Story De-duplication and Mutation

How large is the World Wide Web? Although it is impossible to know the exact size - not to mention the Deep and Dark Web - it was estimated to hold more than a trillion pages in 2008, that, in the data era, was somehow the middle age. Almost a decade later, it is safe to assume that the Internet's collective brain has more neurons than our actual gray matter that's stuffed between our ears. But out of these trillion plus URLs, how many web pages are truly identical, similar, or covering the same topic?

In this chapter, we will de-duplicate and index the GDELT database into stories. Then, we will track stories over time and understand the links between them, how they may mutate and if they could lead to any subsequent event in the near future.

We will cover the following topics:

  • Understand the concept of Simhash to detect near duplicates

  • Build an online de-duplication API

  • Build vectors using TF-IDF and reduce dimensionality using Random Indexing

  • Build...