By Eric Johnson
“ A Developer’s consultant to facts Modeling for SQL Server explains the ideas and perform of information modeling with a readability that makes the expertise available to someone development databases and data-driven applications.
“Eric Johnson and Joshua Jones mix a deep knowing 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 ideas, 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 info modeling is key to making sure that your databases will practice good, scale good, and evolve to satisfy altering necessities. in spite of the fact that, 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 particular real-world strengths and weaknesses frequently result in disastrous functionality.
A Developer’s advisor to facts Modeling for SQL Server is a realistic, SQL Server-specific advisor to info modeling for each developer, architect, and administrator. This e-book will give you helpful start-to-finish tips for designing new databases, remodeling current SQL Server facts types, and migrating databases from different systems.
You’ll commence with a concise, sensible evaluation of the middle info modeling concepts. subsequent, you’ll stroll via standards amassing and realize tips on how to convert requisites into potent SQL Server logical types. 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 better half site.
This publication permits you to
- Understand your info model’s actual parts, from garage to referential integrity
- Provide programmability through saved approaches, user-defined capabilities, triggers, and .NET CLR integration
- Normalize facts versions, one step at a time
- Gather and interpret requisites extra effectively
- Learn an efficient technique for developing logical models
- Overcome modeling difficulties with regards to entities, characteristic, info kinds, garage overhead, functionality, and relationships
- Create actual models—from constructing naming directions via enforcing enterprise ideas and constraints
- Use SQL Server’s distinctive indexing services, and triumph over their limitations
- Create abstraction layers that improve protection, extensibility, and flexibility
Read Online or Download A Developers Guide To Data Modeling For Sql Server PDF
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Additional info for A Developers Guide To Data Modeling For Sql Server
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). Be wary of attaching attributes to one entity that actually belong to another entity. One common mistake is to convert data from physical documentation (such as printed spreadsheets or manuals) into entities and Attributes 25 attributes in a logical model. For example, it is common for customer information to be physically stored with order information.
The various physical elements and the ways they are used are the topics of Chapter 3, Physical Elements of Data Models. Normalization A well-designed data model has some level of normalization. In short, normalization is the process of separating data into logical groupings. Normalization is divided into levels, and each successive level builds on the preceding level. First normal form, notated as 1NF, is the most basic form of normalization. In essence, in 1NF the data is stored in a table and each column contains one type of data.
This combination of PKs and FKs helps ensure consistency in the logical relationships between entities. Domains As you begin building a model, you’ll likely notice that, within the context of the data you are working with, several entities share similar attributes. Often, application- or business-specific pieces of data must remain identical in all entities to ensure consistency. Status, Address, Phone Number, and Email are all examples of attributes that are likely to be identical in multiple entities.