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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

Security ecosystem

We will conclude with a brief rundown of some of the popular security tools we may encounter while developing with Apache Spark - and some advice about when to use them.

Apache sentry

As the Hadoop ecosystem grows ever larger, products such as Hive, HBase, HDFS, Sqoop, and Spark all have different security implementations. This means that duplicate policies are often required across the product stack in order to provide the user with a seamless experience, as well as enforce the overarching security manifest. This can quickly become complicated and time consuming to manage, which often leads to mistakes and even security breaches (whether intentional or otherwise). Apache Sentry pulls many of the mainstream Hadoop products together, particularly with Hive/HS2, to provide fine-grained (up to column level) controls.

Using ACLs is simple, but high maintenance. The setting of permissions for a large number of new files and amending umasks is very cumbersome and time consuming...

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