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 7. Building Communities

With more and more people interacting together and communicating, exchanging information, or simply sharing a common interest in different topics, most data science use cases can be addressed using graph representations. Although very large graphs were, for a long time, only used by the Internet giants, government, and national security agencies, it is becoming more common place to work with large graphs containing millions of vertices. Hence, the main challenge of a data scientist will not necessarily be to detect communities and find influencers on graphs, but rather to do so in a fully distributed and efficient way in order to overcome the constraint of scale. This chapter progresses through building a graph example, at scale, using the persons we identified using NLP extraction described in Chapter 6, Scraping Link-Based External Data.

In this chapter, we will cover the following topics:

  • Use Spark to extract content from Elasticsearch, build a Graph of person...