Book Image

Mastering Spark for Data Science

By : Bifet, Morgan, Amend, Hallett, George
Book Image

Mastering Spark for Data Science

By: Bifet, Morgan, Amend, Hallett, 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 (15 chapters)

GDELT dataset


In order to validate our implementation, we use the GDELT dataset we analyzed in the previous chapter. We extracted all of the communities and spent some time looking at the person names to see whether or not our community clustering was consistent. The full picture of the communities is reported in Figure 7 and has been realized using the Gephi software, where only the top few thousand connections have been imported:

Figure 7: Community detection on January 12

We first observe that most of the communities we detected are totally aligned with the ones we could eyeball on a force-directed layout, giving a good confidence level about the algorithm accuracy.

The Bowie effect

Any well-defined community has been properly identified, and the less obvious ones are the ones surrounding highly connected vertices such as David Bowie. The name David Bowie being heavily mentioned in GDELT articles alongside so many different persons that, on that day of January 12, 2016, it became too large...