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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

Kerberos authentication

Many installations of Apache Spark use Kerberos to provide security and authentication to services such as HDFS and Kafka. It's also especially common when integrating with third-party databases and legacy systems. As a commercial data scientist, at some point, you'll probably find yourself in a situation where you'll have to work with data in a Kerberized environment, so, in this part of the chapter, we'll cover the basics of Kerberos - what it is, how it works, and how to use it.

Kerberos is a third-party authentication technique that's particularly useful where the primary form of communication is over a network, which makes it ideal for Apache Spark. It's used in preference to alternative methods of authentication, for example, username and password, because it provides the following benefits:

  • No passwords are stored in plain text in application configuration files
  • Facilitates centralized management of services, identities, and permissions...
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