Isbn: 9781491952122 - sharing big data safely (21 risultati)

Paperback. Condizione: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New. 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.…

Paperback or Softback. Condizione: New. Sharing Big Data Safely: Managing Data Security. Book.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: new. Paperback. 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." 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. In English.

Condizione: New. 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. Num Pages: 96 pages, colour illustrations. BIC Classification: UN; UR; UTN. Category: (G) General (US: Trade). Dimension: 152 x 231 x 10. Weight in Grams: 156. . 2016. 1st Edition. Paperback. . . . .…

Paperback. Condizione: Brand New. 87 pages. 9.00x6.00x0.25 inches. In Stock.

Condizione: New. 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. Num Pages: 96 pages, colour illustrations. BIC Classification: UN; UR; UTN. Category: (G) General (US: Trade). Dimension: 152 x 231 x 10. Weight in Grams: 156. . 2016. 1st Edition. Paperback. . . . . Books ship from the US and Ireland.…

Paperback. Condizione: New. 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.…

Taschenbuch. Condizione: Neu. Neuware - 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.…

Paperback. Condizione: new. Paperback. 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." 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Kartoniert / Broschiert. Condizione: New. 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 an.

Paperback. Condizione: Brand New. 87 pages. 9.00x6.00x0.25 inches. In Stock. This item is printed on demand.

Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 175.