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

Formulating a plan of action


Having inspected the GDELT schemas, we now need to make some decisions around what data we are going to use, and make sure we justify that usage based on our hypotheses. This is a critical stage as there are many areas to consider, and at the very least we need to:

  • Ensure that our hypotheses are clear so that we have a known starting point

  • Ensure that we are clear about how we are going to implement the hypotheses, and determine an action plan

  • Ensure that we use enough appropriate data to meet our action plan; scope the data usage to ensure we can produce a conclusion within a given time frame, for example, using all GDELT data would be great, but is probably not reasonable unless a large processing cluster is available. On the other hand using one day is clearly not enough to gauge any patterns over time

  • Formulate a plan B in case our initial results are not conclusive

Our second hypothesis is about the detail of the events; for the purposes of clarity, in this chapter...