Learn effective and scalable database design techniques in a SQL Server 2016 and higher environment. This book is revised to cover in-memory online transaction processing, temporal data storage, row-level security, durability enhancements, and other design-related features that are new or changed in SQL Server 2016.
Designing an effective and scalable database using SQL Server is a task requiring skills that have been around for forty years coupled with technology that is constantly changing. Pro SQL Server Relational Database Design and Implementation covers everything from design logic that business users will understand, all the way to the physical implementation of design in a SQL Server database. Grounded in best practices and a solid understanding of the underlying theory, Louis Davidson shows how to "get it right" in SQL Server database design and lay a solid groundwork for the future use of valuable business data.
The pace of change in relational database management systems has been tremendous these past few years. Whereas in the past it was enough to think about optimizing data residing on spinning hard drives, today one also must consider solid-state storage as well as data that are constantly held in memory and never written to disk at all except as a backup. Furthermore, there is a trend toward hybrid cloud and on-premise database configurations as well a move toward preconfigured appliances. Pro SQL Server Relational Database Design and Implementation guides in the understanding of these massive changes and in their application toward sound database design.
- Gives a solid foundation in best practices and relational theory
- Covers the latest implementation features in SQL Server 2016
- Helps you master in-memory OLTP and use it effectively
- Takes you from conceptual design to an effective, physical implementation
- Develop conceptual models of client data using interviews and client documentation
- Recognize and apply common database design patterns
- Normalize data models to enhance scalability and the long term use of valuable data
- Translate conceptual models into high–performing SQL Server databases
- Secure and protect data integrity as part of meeting regulatory requirements
- Create effective indexing to speed query performance
