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Mastering Spark for Data Science

You're reading from   Mastering Spark for Data Science Lightning fast and scalable data science solutions

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
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Authors (5):
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David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
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Matthew Hallett
Antoine Amend Antoine Amend
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Antoine Amend
Andrew Morgan Andrew Morgan
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Andrew Morgan
Albert Bifet Albert Bifet
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Albert Bifet
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Table of Contents (15) Chapters Close

Preface 1. The Big Data Science Ecosystem 2. Data Acquisition FREE CHAPTER 3. Input Formats and Schema 4. Exploratory Data Analysis 5. Spark for Geographic Analysis 6. Scraping Link-Based External Data 7. Building Communities 8. Building a Recommendation System 9. News Dictionary and Real-Time Tagging System 10. Story De-duplication and Mutation 11. Anomaly Detection on Sentiment Analysis 12. TrendCalculus 13. Secure Data 14. Scalable Algorithms

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...

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