By Eric Johnson
“ A Developer’s consultant to information Modeling for SQL Server explains the strategies and perform of knowledge modeling with a readability that makes the know-how obtainable to someone construction databases and data-driven applications.
“Eric Johnson and Joshua Jones mix a deep realizing of the technology of knowledge modeling with the paintings that incorporates years of expertise. If you’re new to information modeling, or locate the necessity to brush up on its ideas, this ebook is for you.”
—Peter Varhol, government Editor, Redmond Magazine
Model SQL Server Databases That paintings higher, Do extra, and Evolve extra easily
Effective information modeling is vital to making sure that your databases will practice good, scale good, and evolve to fulfill altering specifications. notwithstanding, 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 distinctive real-world strengths and weaknesses usually bring about disastrous functionality.
A Developer’s advisor to facts Modeling for SQL Server is a pragmatic, SQL Server-specific consultant to facts modeling for each developer, architect, and administrator. This ebook provides you with worthwhile start-to-finish tips for designing new databases, remodeling current SQL Server information versions, and migrating databases from different systems.
You’ll start with a concise, functional evaluate of the middle information modeling recommendations. subsequent, you’ll stroll via standards accumulating and realize tips on how to convert requisites into potent SQL Server logical types. eventually, you’ll systematically rework 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 significant other website.
This booklet allows you to
- Understand your facts model’s actual parts, from garage to referential integrity
- Provide programmability through saved systems, user-defined capabilities, triggers, and .NET CLR integration
- Normalize information versions, one step at a time
- Gather and interpret necessities extra effectively
- Learn a good method for developing logical models
- Overcome modeling difficulties relating to entities, characteristic, facts forms, garage overhead, functionality, and relationships
- Create actual models—from constructing naming instructions via imposing enterprise principles and constraints
- Use SQL Server’s detailed indexing services, and conquer their limitations
- Create abstraction layers that increase safety, extensibility, and flexibility
Read 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
Two character large object (CLOB) data types handle this information: text and ntext. These two data types are designed to handle large amounts of character data in a single field. Again, as with the other character data types, the n prefix indicates whether or not the data is being stored in the Unicode format. Choose these data types when you will have very large amounts of alphanumeric text stored as a single attribute in an entity. Dates and Times Nearly every data model in existence requires that some entities have attributes that are related to dates and times.
Sure, if you go out and buy the most expensive server known to humans and load it up with gigs upon gigs of RAM—and as many processors as you can without setting the thing on fire—you will get your database to perform better. But many design 14 Chapter 1 Data Modeling Overview decisions are about trade-offs: do you really want to spend hundreds or thousands of dollars for a 10 percent performance boost? In the long run, a better solution can be to redesign a poorly designed database. The horrible testing database we discussed probably wouldn’t have scaled very well.
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).