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.
