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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

Introducing data virtualization

Data virtualization is the name given to any approach that enables you to query across disparate systems from a single source without having to know where that data physically resides.

Queries can be executed against a wide variety of relational and non-relational datasets, some of which may have their own compute engines, while others may not. When a remote compute engine exists, data virtualization technology enables you to push down computation to that data source. Advanced data virtualization engines also provide additional transparent caching of data to reduce the impact on the source system. These capabilities achieve a number of important benefits, which are summarized here for you:

  • Consolidate query processing at the source, reducing spend on resources.
  • Manage a single, semantic, and logical view of your data, avoiding duplication.
  • Address data issues at the source, improving data quality.
  • Query disparate data in real...
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