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Mastering Spark for Data Science

You're reading from  Mastering Spark for Data Science

Product type Book
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Pages 560 pages
Edition 1st Edition
Languages
Authors (4):
Andrew Morgan Andrew Morgan
Profile icon Andrew Morgan
Antoine Amend Antoine Amend
Profile icon Antoine Amend
Matthew Hallett Matthew Hallett
Profile icon Matthew Hallett
David George David George
Profile icon David George
View More author details

Table of Contents (22) Chapters

Mastering Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
The Big Data Science Ecosystem Data Acquisition Input Formats and Schema Exploratory Data Analysis Spark for Geographic Analysis Scraping Link-Based External Data Building Communities Building a Recommendation System News Dictionary and Real-Time Tagging System Story De-duplication and Mutation Anomaly Detection on Sentiment Analysis TrendCalculus Secure Data Scalable Algorithms

Your Secure Responsibility


Now that we've covered the common security use cases and discussed some of the tools that a data scientist needs to be aware of in their everyday activities, there's one last important item to note. While in their custody, the responsibility for data, including its security and integrity, lies with the data scientist. This is usually true whether or not you are explicitly told. Therefore, it is crucial that you take this responsibility seriously and take all the necessary precautions when handling and processing data. If needed, also be ready to communicate to others their responsibility. We all need to ensure that we are not held responsible for a breach off-site; this can be achieved by highlighting the issue or, indeed, even having a written contract with the off-site service provider outlining their security arrangements. To see a real-world example of what can go wrong when you don't pay proper attention to due diligence, have a look at some security notes...

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