By Eric Johnson
“ A Developer’s advisor to information Modeling for SQL Server explains the strategies and perform of knowledge modeling with a readability that makes the know-how available to someone construction databases and data-driven applications.
“Eric Johnson and Joshua Jones mix a deep realizing of the technology of information modeling with the artwork that incorporates years of expertise. If you’re new to info modeling, or locate the necessity to brush up on its thoughts, this e-book is for you.”
—Peter Varhol, government Editor, Redmond Magazine
Model SQL Server Databases That paintings larger, Do extra, and Evolve extra easily
Effective facts modeling is vital to making sure that your databases will practice good, scale good, and evolve to fulfill altering necessities. although, if you’re modeling databases to run on Microsoft SQL Server 2008 or 2005, theoretical or platform-agnostic info modeling wisdom isn’t sufficient: types that don’t replicate SQL Server’s specific real-world strengths and weaknesses usually result in disastrous functionality.
A Developer’s consultant to facts Modeling for SQL Server is a realistic, SQL Server-specific advisor to info modeling for each developer, architect, and administrator. This booklet provides you with worthwhile start-to-finish counsel for designing new databases, remodeling current SQL Server facts types, and migrating databases from different systems.
You’ll commence with a concise, useful evaluate of the center facts modeling strategies. subsequent, you’ll stroll via necessities accumulating and observe find out how to convert standards into powerful SQL Server logical versions. ultimately, you’ll systematically remodel these logical types into actual versions that utilize SQL Server’s prolonged performance. All of this book’s many examples can be found for obtain from a spouse site.
This ebook permits you to
- Understand your info model’s actual parts, from garage to referential integrity
- Provide programmability through saved strategies, user-defined features, triggers, and .NET CLR integration
- Normalize facts versions, one step at a time
- Gather and interpret standards extra effectively
- Learn an efficient method for developing logical models
- Overcome modeling difficulties concerning entities, characteristic, information varieties, garage overhead, functionality, and relationships
- Create actual models—from setting up naming instructions via enforcing enterprise principles and constraints
- Use SQL Server’s specified indexing services, and conquer their limitations
- Create abstraction layers that increase defense, extensibility, and flexibility
Read Online or Download A Developers Guide To Data Modeling For Sql Server PDF
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Extra info for A Developers Guide To Data Modeling For Sql Server
Additionally, we look at a few things, such as subtype and supertype modeling, that aren’t necessarily problems but can be tricky. Building the Physical Model Once you have the logical model hammered out, you translate it into a physical model, and we turn to that topic in Part IV. A physical model is made up of the tables and other physical objects of your RDBMS. Much of the work of creating your database has been completed during the logical modeling, but that doesn’t mean you should take the physical model lightly.
Another reason for an abstraction layer is to shield users and applications from database changes. If you rearrange tables, as long as you update the abstraction layer to point at the new table structure, your users and applications will never be the wiser. This means less broken code and easier migration of code when changes need to be made. We talk in great detail about the benefits of an abstraction layer and explain how to build one in Chapter 11, Creating an Abstraction Layer in SQL Server.
Attributes For each entity, there are specific pieces of information that describe it. These are the attributes of that entity. For example, suppose you need to create an entity to store all the pertinent information about hats. You name the entity Hats, and then you decide what information, or attributes, you need to store about hats: color, manufacturer, style, material, and the like. When you construct a model, you define a collection of attributes that stores the data for each entity. The definition of an attribute is made up of its name, description, purpose, and data type (which we talk about in the next section).
A Developers Guide To Data Modeling For Sql Server by Eric Johnson