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

Chapter 6. Scraping Link-Based External Data

This chapter aims to explain a common pattern for enhancing local data with external content found at URLs or over APIs. Examples of this are when URLs are received from GDELT or Twitter. We offer readers a tutorial using the GDELT news index service as a source of news URLs, demonstrating how to build a web scale news scanner that scrapes global breaking news of interest from the Internet. We explain how to build this specialist web scraping component in a way that overcomes the challenges of scale. In many use cases, accessing the raw HTML content is not sufficient enough to provide deeper insights into emerging global events. An expert data scientist must be able to extract entities out of that raw text content to help build the context needed track broader trends.

In this chapter, we will cover the following topics:

  • Create a scalable web content fetcher using the Goose library

  • Leverage the Spark framework for Natural Language Processing (NLP...