By Eric Johnson
“ A Developer’s advisor to facts Modeling for SQL Server explains the innovations and perform of knowledge modeling with a readability that makes the expertise obtainable to somebody construction databases and data-driven applications.
“Eric Johnson and Joshua Jones mix a deep figuring out of the technological know-how of knowledge modeling with the artwork that includes years of expertise. If you’re new to facts modeling, or locate the necessity to brush up on its ideas, this booklet is for you.”
—Peter Varhol, govt Editor, Redmond Magazine
Model SQL Server Databases That paintings greater, 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 satisfy altering necessities. 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 mirror SQL Server’s particular real-world strengths and weaknesses usually 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 publication will give you priceless start-to-finish counsel for designing new databases, remodeling latest SQL Server information versions, and migrating databases from different systems.
You’ll commence with a concise, sensible assessment of the center info modeling strategies. subsequent, you’ll stroll via specifications accumulating and notice the best way to convert necessities into potent SQL Server logical versions. eventually, you’ll systematically remodel these logical types into actual types that utilize SQL Server’s prolonged performance. All of this book’s many examples can be found for obtain from a spouse site.
This e-book permits you to
- Understand your information model’s actual components, from garage to referential integrity
- Provide programmability through saved approaches, user-defined capabilities, triggers, and .NET CLR integration
- Normalize information types, one step at a time
- Gather and interpret standards extra effectively
- Learn a good method for developing logical models
- Overcome modeling difficulties on the topic of entities, characteristic, information varieties, garage overhead, functionality, and relationships
- Create actual models—from developing naming directions via enforcing enterprise principles and constraints
- Use SQL Server’s designated indexing features, and conquer their limitations
- Create abstraction layers that increase safety, extensibility, and flexibility
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Extra info for A developer's guide to data modeling for SQL server : covering SQL server 2005 and 2008
Many aspects of data modeling are counterintuitive, and following your intuition can lead to some of these problems. We go through these problems and talk about why people fall into these traps, how you can avoid them, and the appropriate ways to work around them. 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.
You don’t want to store multiple records for a single customer merely to account for a different phone number; that defeats the purpose of using a relational database, because it introduces problems with data retrieval. Instead, you can create a new entity that holds phone numbers, with a relationship to the Customer entity (based on the primary key of the Customer), that allows you to identify all phone numbers for a single customer. The resultant entity might have multiple entries for each customer, but it stores only a unique identifier—CustomerID—and of course the phone number.
Many of these data types are similar to the types we looked at in Chapter 2. In this section, we look at each of the SQL Server data types and talk about how the SQL Server engine handles and stores them. When you build your model, it is important to understand how much space each data type requires. The difference between a data type that needs 2 bytes versus one that requires 4 bytes may seem insignificant, but when you multiply the extra 2 bytes over millions or billions of rows, you could end up needing tens or hundreds of gigabytes of additional storage.