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A Developer's Guide to Data Modeling for SQL Server: Covering SQL Server 2005 and 2008
""A Developer's Guide to Data Modeling for SQL Server" explains the concepts and practice of data modeling with a clarity that makes the technology accessible to anyone building databases and data-driven applications.
"Eric Johnson and Joshua Jones combine a deep understanding of the science of data modeling with the art that comes with years of experience. If you're new to data modeling, or find the need to brush up on its concepts, this book is for you."
--Peter Varhol, Executive Editor, "Redmond Magazine" Model SQL Server Databases That Work Better, Do More, and Evolve More Smoothly
Effective data modeling is essential to ensuring that your databases will perform well, scale well, and evolve to meet changing requirements. However, if you're modeling databases to run on Microsoft SQL Server 2008 or 2005, theoretical or platform-agnostic data modeling knowledge isn't enough: models that don't reflect SQL Server's unique real-world strengths and weaknesses often lead to disastrous performance.
"A Developer's Guide to Data Modeling for SQL Server"is a practical, SQL Server-specific guide to data modeling for every developer, architect, and administrator. This book offers you invaluable start-to-finish guidance for designing new databases, redesigning existing SQL Server data models, and migrating databases from other platforms.
You'll begin with a concise, practical overview of the core data modeling techniques. Next, you'll walk through requirements gathering and discover how to convert requirements into effective SQL Server logical models. Finally, you'll systematically transform those logical models into physical models that make the most of SQL Server's extended functionality. All of this book's many examples are available for download from a companion Web site.
This book enables you to
Understand your data model's physical elements, from storage to referential integrityProvide programmability via stored procedures, user-defined functions, triggers, and .NET CLR integrationNormalize data models, one step at a timeGather and interpret requirements more effectivelyLearn an effective methodology for creating logical modelsOvercome modeling problems related to entities, attribute, data types, storage overhead, performance, and relationships Create physical models--from establishing naming guidelines through implementing business rules and constraintsUse SQL Server's unique indexing capabilities, and overcome their limitationsCreate abstraction layers that enhance security, extensibility, and flexibility
"Eric Johnson and Joshua Jones combine a deep understanding of the science of data modeling with the art that comes with years of experience. If you're new to data modeling, or find the need to brush up on its concepts, this book is for you."
--Peter Varhol, Executive Editor, "Redmond Magazine" Model SQL Server Databases That Work Better, Do More, and Evolve More Smoothly
Effective data modeling is essential to ensuring that your databases will perform well, scale well, and evolve to meet changing requirements. However, if you're modeling databases to run on Microsoft SQL Server 2008 or 2005, theoretical or platform-agnostic data modeling knowledge isn't enough: models that don't reflect SQL Server's unique real-world strengths and weaknesses often lead to disastrous performance.
"A Developer's Guide to Data Modeling for SQL Server"is a practical, SQL Server-specific guide to data modeling for every developer, architect, and administrator. This book offers you invaluable start-to-finish guidance for designing new databases, redesigning existing SQL Server data models, and migrating databases from other platforms.
You'll begin with a concise, practical overview of the core data modeling techniques. Next, you'll walk through requirements gathering and discover how to convert requirements into effective SQL Server logical models. Finally, you'll systematically transform those logical models into physical models that make the most of SQL Server's extended functionality. All of this book's many examples are available for download from a companion Web site.
This book enables you to
Understand your data model's physical elements, from storage to referential integrityProvide programmability via stored procedures, user-defined functions, triggers, and .NET CLR integrationNormalize data models, one step at a timeGather and interpret requirements more effectivelyLearn an effective methodology for creating logical modelsOvercome modeling problems related to entities, attribute, data types, storage overhead, performance, and relationships Create physical models--from establishing naming guidelines through implementing business rules and constraintsUse SQL Server's unique indexing capabilities, and overcome their limitationsCreate abstraction layers that enhance security, extensibility, and flexibility
298 pages, Paperback
First published June 1, 2008
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Displaying 1 of 1 review
January 29, 2017
This is a nice book that sits in between academic texts focused solely on creating a logical data model and detailed texts aimed at DBAs that talk about how to manage a SQL Server database. In this book, Johnson and Jones talk about data modeling with the perspective being that you're developing a logical data model that will be physically implemented in a SQL Server database.
The approach gives us a nice book that bridges the gap between data modeling and SQL Server database management. The authors give us a high-level process for data modeling, while touching on various aspects of SQL Server that are likely to impact choices made during the data modeling process.
My only complaint is that the book didn't cover more patterns common in data modeling, other than typical 1-M and M-M relationships. For that, however, you can find other books that serve as cookbooks for typical data modeling patterns.
The approach gives us a nice book that bridges the gap between data modeling and SQL Server database management. The authors give us a high-level process for data modeling, while touching on various aspects of SQL Server that are likely to impact choices made during the data modeling process.
My only complaint is that the book didn't cover more patterns common in data modeling, other than typical 1-M and M-M relationships. For that, however, you can find other books that serve as cookbooks for typical data modeling patterns.
Displaying 1 of 1 review