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

Data security


The final piece to our data architecture is security, and in this chapter we will discover that data security is always important, and the reasons for this. Given the huge increase in the volume and variety of data in recent times, caused by many factors, but in no small part due to the popularity of the Internet and related technologies, there is a growing need to provide fully scalable and secure solutions. We are going to explore those solutions along with the confidentiality, privacy, and legal concerns associated with the storing, processing, and handling of data; we will relate these to the tools and techniques introduced in previous chapters.

We will continue on by explaining the technical issues involved in securing data at scale and introduce ideas and techniques that tackle these concerns using a variety of access, classification, and obfuscation strategies. As in previous chapters, ideas are demonstrated with examples using the Hadoop ecosystem, and public cloud infrastructure...