A Developers Guide To Data Modeling For Sql Server by Eric Johnson PDF

By Eric Johnson

ISBN-10: 0321497643

ISBN-13: 9780321497642

A Developer’s advisor to facts Modeling for SQL Server explains the suggestions and perform of knowledge modeling with a readability that makes the know-how obtainable to a person development databases and data-driven applications.

“Eric Johnson and Joshua Jones mix a deep realizing of the technological know-how of information modeling with the paintings that includes years of expertise. If you’re new to info modeling, or locate the necessity to brush up on its strategies, this booklet is for you.”
Peter Varhol, govt Editor, Redmond Magazine

Model SQL Server Databases That paintings larger, 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 fulfill altering standards. besides the fact that, if you’re modeling databases to run on Microsoft SQL Server 2008 or 2005, theoretical or platform-agnostic information modeling wisdom isn’t sufficient: types that don’t replicate SQL Server’s distinctive real-world strengths and weaknesses frequently bring about disastrous functionality.

A Developer’s consultant to information Modeling for SQL Server is a realistic, SQL Server-specific advisor to information modeling for each developer, architect, and administrator. This publication provides you with important start-to-finish advice for designing new databases, remodeling latest SQL Server information versions, and migrating databases from different systems.

You’ll start with a concise, sensible review of the middle information modeling recommendations. subsequent, you’ll stroll via standards collecting and realize how you can convert standards 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 site.

This publication permits you to

  • Understand your information model’s actual components, from garage to referential integrity
  • Provide programmability through saved techniques, user-defined features, triggers, and .NET CLR integration
  • Normalize info types, one step at a time
  • Gather and interpret necessities extra effectively
  • Learn a good method for growing logical models
  • Overcome modeling difficulties with regards to entities, characteristic, facts forms, garage overhead, functionality, and relationships
  • Create actual models—from setting up naming guidance via imposing enterprise principles and constraints
  • Use SQL Server’s certain indexing services, and triumph over their limitations
  • Create abstraction layers that improve safeguard, extensibility, and flexibility

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Extra info for A Developers Guide To Data Modeling For Sql Server

Sample text

Tables have columns, which contain the information about the data in the table rows. SQL Server also provides primary and foreign keys (defined in Chapter 2), which allow you to define the relationship between two tables. At first glance, tables, columns, and keys might seem to be the same as the logical elements, but there are important differences. Logical 16 Chapter 1 Data Modeling Overview elements simply describe the groupings of data as they might exist in the real world; in contrast, physical elements actually store the data in a database.

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).

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. This practice could lead to the belief that customer data, such as address or phone number, is an attribute of an order. However, customer is an entity in and of itself, as is an order. Storing the customer attributes with the order entity would complicate storage and data retrieval and possibly lead to a design that is difficult to scale.

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A Developers Guide To Data Modeling For Sql Server by Eric Johnson

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