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

Exploring GDELT


A large part of the EDA journey is obtaining and documenting the sources of data, and GDELT content is no exception. After researching the GKG datasets, we discovered that it was challenging just to document the actual sources of data we should be using. In the following sections, we provide a comprehensive listing of the resources we located for use, which will need to be run in the examples.

Note

A cautionary note on download times: using a typical 5 Mb home broadband, 2000 GKG files takes approximately 3.5 hours to download. Given that the GKG English language files alone have over 40,000 files, this could take a while to download.

GDELT GKG datasets

We should be using the latest GDELT data feed, version 2.1 as of December 2016. The main documentation for this data is here:

http://data.gdeltproject.org/documentation/GDELT-Global_Knowledge_Graph_Codebook-V2.1.pdf

In the following section, we have included the data and secondary references to look up tables, and further documentation...