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Introducing Microsoft SQL Server 2019

You're reading from  Introducing Microsoft SQL Server 2019

Product type Book
Published in Apr 2020
Publisher Packt
ISBN-13 9781838826215
Pages 488 pages
Edition 1st Edition
Languages
Authors (8):
Kellyn Gorman Kellyn Gorman
Profile icon Kellyn Gorman
Allan Hirt Allan Hirt
Profile icon Allan Hirt
Dave Noderer Dave Noderer
Profile icon Dave Noderer
Mitchell Pearson Mitchell Pearson
Profile icon Mitchell Pearson
James Rowland-Jones James Rowland-Jones
Profile icon James Rowland-Jones
Dustin Ryan Dustin Ryan
Profile icon Dustin Ryan
Arun Sirpal Arun Sirpal
Profile icon Arun Sirpal
Buck Woody Buck Woody
Profile icon Buck Woody
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Toc

Table of Contents (15) Chapters close

Preface 1. Optimizing for performance, scalability and real‑time insights 2. Enterprise Security 3. High Availability and Disaster Recovery 4. Hybrid Features – SQL Server and Microsoft Azure 5. SQL Server 2019 on Linux 6. SQL Server 2019 in Containers and Kubernetes 7. Data Virtualization 8. Machine Learning Services Extensibility Framework 9. SQL Server 2019 Big Data Clusters 10. Enhancing the Developer Experience 11. Data Warehousing 12. Analysis Services 13. Power BI Report Server 14. Modernization to the Azure Cloud

Management

Management of any system involves security, safety, monitoring and performance, and optimization. In the case of SQL Server Machine Learning Services, the safety portion (backups, availability, and the like) are part of the database environment. Performance tuning involves optimizing the T-SQL and language-specific code and calls. That leaves you with a specific set of processes and tools for security, as well as monitoring and performance.

Security

For the most part, the security for using Machine Learning Services follows the same model as other SQL Server securables. The person or SQL Server principal calling the Machine Learning Services extensibility framework functions needs to be a Windows or SQL Server database user, must have access to the tables or views they are passing in, the ability to write data out (if they do that with the returned data), and be able to create stored procedures if they are making new code to run the models.

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