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

Access


We have thus far concentrated only on the specific ideas of ensuring that a user is who they say they are and that only the correct users can view and use data. However, once we have taken the appropriate steps and confirmed these details, we still need to ensure that this data is secure when the user is actually using it; there are a number of areas to consider:

  • Is the user allowed to see all of the information in the data? Perhaps they are to be limited to certain rows, or even certain parts of certain rows.

  • Is the data secure when the user runs analytics across it? We need to ensure that the data isn't transmitted as plain text and therefore open to man-in-the-middle attacks.

  • Is the data secure once the user has completed their task? There's no point in ensuring that the data is super secure at all stages, only to write plain text results to an insecure area.

  • Can conclusions be made from the aggregation of data? Even if the user only has access to certain rows of a dataset, let's say...