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Sharing Big Data Safely: Managing Data Security

Unknown Author
4.9/5 (27997 ratings)
Read Now Download Now ⏭ listen AudioBook
Description: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."We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Sharing Big Data Safely: Managing Data Security. To get started finding Sharing Big Data Safely: Managing Data Security, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.
Pages
—
Format
PDF, EPUB & Kindle Edition
Publisher
—
Release
—
ISBN
1491952121

Sharing Big Data Safely: Managing Data Security

Unknown Author
4.4/5 (1290744 ratings)
Read Now Download now ⏭ listen AudioBook
Description: 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."We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Sharing Big Data Safely: Managing Data Security. To get started finding Sharing Big Data Safely: Managing Data Security, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.
Pages
—
Format
PDF, EPUB & Kindle Edition
Publisher
—
Release
—
ISBN
1491952121

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