Sharing Big Data Safely

Managing Data Security

Nonfiction, Computers, Database Management, Data Processing
Cover of the book Sharing Big Data Safely by Ted Dunning, Ellen Friedman, O'Reilly Media
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Author: Ted Dunning, Ellen Friedman ISBN: 9781491953631
Publisher: O'Reilly Media Publication: September 15, 2015
Imprint: O'Reilly Media Language: English
Author: Ted Dunning, Ellen Friedman
ISBN: 9781491953631
Publisher: O'Reilly Media
Publication: September 15, 2015
Imprint: O'Reilly Media
Language: English

Many big data-driven companies today are moving to protect certain types of data against intrusion, leaks, or unauthorized eyes. But how do you lock down data while granting access to people who need to see it? In this practical book, authors Ted Dunning and Ellen Friedman offer two novel and practical solutions that you can implement right away.

Ideal for both technical and non-technical decision makers, group leaders, developers, and data scientists, this book shows you how to:

  • Share original data in a controlled way so that different groups within your organization only see part of the whole. You’ll learn how to do this with the new open source SQL query engine Apache Drill.
  • Provide synthetic data that emulates the behavior of sensitive data. This approach enables external advisors to work with you on projects involving data that you can't show them.

If you’re intrigued by the synthetic data solution, explore the log-synth program that Ted Dunning developed as open source code (available on GitHub), along with how-to instructions and tips for best practice. You’ll also get a collection of use cases.

Providing lock-down security while safely sharing data is a significant challenge for a growing number of organizations. With this book, you’ll discover new options to share data safely without sacrificing security.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Many big data-driven companies today are moving to protect certain types of data against intrusion, leaks, or unauthorized eyes. But how do you lock down data while granting access to people who need to see it? In this practical book, authors Ted Dunning and Ellen Friedman offer two novel and practical solutions that you can implement right away.

Ideal for both technical and non-technical decision makers, group leaders, developers, and data scientists, this book shows you how to:

If you’re intrigued by the synthetic data solution, explore the log-synth program that Ted Dunning developed as open source code (available on GitHub), along with how-to instructions and tips for best practice. You’ll also get a collection of use cases.

Providing lock-down security while safely sharing data is a significant challenge for a growing number of organizations. With this book, you’ll discover new options to share data safely without sacrificing security.

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