By Eric Johnson
“ A Developer’s advisor to information Modeling for SQL Server explains the innovations and perform of information modeling with a readability that makes the expertise obtainable to a person development databases and data-driven applications.
“Eric Johnson and Joshua Jones mix a deep realizing of the technology of knowledge modeling with the artwork that incorporates years of expertise. If you’re new to facts modeling, or locate the necessity to brush up on its strategies, this publication is for you.”
—Peter Varhol, govt Editor, Redmond Magazine
Model SQL Server Databases That paintings greater, 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 requisites. in spite of the fact that, if you’re modeling databases to run on Microsoft SQL Server 2008 or 2005, theoretical or platform-agnostic facts modeling wisdom isn’t adequate: versions that don’t replicate SQL Server’s distinct real-world strengths and weaknesses usually bring about disastrous functionality.
A Developer’s advisor to info Modeling for SQL Server is a realistic, SQL Server-specific advisor to info modeling for each developer, architect, and administrator. This ebook provides you with useful start-to-finish tips for designing new databases, remodeling current SQL Server information types, and migrating databases from different structures.
You’ll commence with a concise, sensible evaluation of the middle info modeling thoughts. subsequent, you’ll stroll via requisites amassing and notice the best way to convert specifications into powerful SQL Server logical versions. ultimately, you’ll systematically rework these logical versions 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 website.
This booklet permits you to
- Understand your info model’s actual parts, from garage to referential integrity
- Provide programmability through saved strategies, user-defined services, triggers, and .NET CLR integration
- Normalize info versions, one step at a time
- Gather and interpret necessities extra effectively
- Learn a good method for growing logical models
- Overcome modeling difficulties on the topic of entities, characteristic, facts varieties, garage overhead, functionality, and relationships
- Create actual models—from constructing naming directions via imposing company ideas and constraints
- Use SQL Server’s particular indexing features, and triumph over their limitations
- Create abstraction layers that improve protection, extensibility, and flexibility
Read Online or Download A developer's guide to data modeling for SQL server : covering SQL server 2005 and 2008 PDF
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Extra resources for A developer's guide to data modeling for SQL server : covering SQL server 2005 and 2008
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).
With a one-to-one relationship, the cardinality is implied. You are clearly stating that for every one record in the parent entity, there might be one record in the child entity. ” The cardinality of a one-to-one relationship is notated as [1:1]. ” In most relationships, the “zero or more to many” interpretation is correct, so be sure to specify and document the alternative definition if it’s used in your model. ” In this case, the “zero or more to zero or more records” cardinality is almost always implied, although you could specify that there must be at least one record in each entity.
It is important to remember that these relationships are logical; physical implementation is another step, as discussed later in Chapter 9. One-to-One Relationships Simply put, a one-to-one relationship between two entities is, as the name implies, a direct match between the entities. For each record in the first entity, there is one matching record in the second entity, no more and no less. For example, think of two people playing catch with a ball. There is one thrower and one receiver. There cannot be more than one thrower, and there cannot be more than one catcher (in terms of someone actually catching the ball).
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