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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

Encryption

Arguably the most obvious and well known method of protecting data is encryption. We would use this whether our data is in transit or at rest, so, virtually all of the time, apart from when the data is actually being processed inside memory. The mechanics of encryption are different depending upon the state of the data.

Data at rest

Our data will always need to be stored somewhere, whether it be HDFS, S3, or local disk. If we have taken all of the precautions of ensuring that users are authorized and authenticated, there is still the issue of plain text actually existing on the disk. With direct access to the disk, either physically or by accessing it through a lower level in the OSI stack, it is fairly trivial to stream the entire contents and glean the plain text data.

If we encrypt data, then we are protected from this type of attack. The encryption can also exist at different levels, either by encrypting the data at the application layer using software, or by encrypting it at...

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