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 disposal


Secure data should have an agreed life cycle. This will be set by a data authority when working in a commercial context, and it will dictate what state the data should be in at any given point during that life cycle. For example, a particular dataset may be labeled as sensitive - requires encryption for the first year of its life, followed by private - no encryption, and finally, disposal. The lengths of time and the rules applied will entirely depend upon the organization and the data itself - some data expires after just a few days, some after fifty years. The life cycle ensures that everyone knows exactly how the data should be treated, and it also ensures that older data is not needlessly taking up valuable disk space or breaching any data protection laws.

The correct disposal of data from secure systems is perhaps one of the most mis-understood areas of data security. Interestingly, it doesn't always involve a complete and/or destructive removal process. Examples where...