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

Summary


In this chapter, we have introduced the concepts of storing data in a spatio-temporal way so that we can use GeoMesa and GeoServer to create and run queries. We have shown these queries executed in both the tools themselves and in a programmatic way, leveraging GeoServer to display results. Further, we have demonstrated how to merge different artifacts to create insights purely from the raw GDELT events, before any follow-on processing. Following on from GeoMesa, we have touched upon the highly complex world of oil pricing and worked on a simple algorithm to estimate weekly oil changes. Whilst it is not reasonable to create an accurate model with the time and resources available, we have explored a number of areas of concern and attempted to address these, at least at a high level, in order to give an insight into possible approaches that can be made in this problem space.

Throughout the chapter, we have introduced a number of key Spark libraries and functions, the key area being...