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They are also frequently used to store money amounts Floating point numbers Columns with this data type are used to store scientific numbers that can be calculated approximately, such as weights and distances Floating point numbers can represent a larger range of values than decimal numbers but can produce round-off errors in computations Fixed-length character strings Columns holding this type of data typically store names of people and companies, addresses, descriptions, and so on Variable-length character strings This data type allows a column to store character strings that vary in length from row to row, up to some maximum length (The SQL1 standard permitted only fixed-length character strings, which are easier for the DBMS to process but can waste considerable space) Money amounts Many SQL products support a MONEY or CURRENCY type, which is usually stored as a decimal or floating point number.

Using these generic definitions, it will be possible to address dimensional design techniques in a manner befitting any data warehouse architecture, including yours.

Notes Uses local AppleTalk zone for communications. Supports SPP communications; only available on Intel platform. Uses any protocol type; data and authentication encryption are possible. Works on TCP/IP, NetBEUI, or IPX/SPX networks. Can connect to NetWare Bindery. Uses TCP port 1433 by default and can work over the Internet; also supports MS Proxy Server.

Summary

Having a distinct money type allows the DBMS to properly format money amounts when they are displayed..

Table 4-5.

Dimensional design figures into data warehouse architectures in very different ways. Inmon s Corporate Information Factory consolidates information from throughout the enterprise into a central repository called an enterprise data warehouse. It is characterized by a third normal form design, and it is not queried directly by warehouse applications. Outward from this hub radiate data marts, each tailored to the needs and viewpoint of a particular business group. These data marts sport a dimensional design and are queried by data warehouse applications. Kimball s dimensional data warehouse also has an enterprise focus. It brings together data from throughout the enterprise into a central repository called the dimensional data warehouse. This repository is characterized by a dimensional design and may be queried directly. A data mart is a logical construct, or subset of the tables. Optionally, separate dimensional structures may be built to support departmental needs. The stand-alone data mart addresses the needs within a subject area, without an enterprise context. It may leverage dimensional design, or it may follow other techniques. The limitations of this approach may be accepted by an organization in a trade-off for rapid access to results and reduced costs. However, they should not be interpreted as indicating shortcomings of data marts or dimensional design. Each of these architectures has a place for the star schema. Generalized definitions for key terms like data warehouse and data mart allow this book to deal with dimensional design in a manner that is applicable to all architectures.

can be modified after SQL Server installation by using the Client Network Utility and Server Network Utility programs in the Microsoft SQL Server 7 program group.

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The quotation from Bill Inmon comes from his article The Problem with Dimensional Modeling, DM Review, May 2000. Much has been written on Inmon s Corporate Information Factory architecture. The most comprehensive coverage is provided in the book The Corporate Information Factory, Second Edition by W. H. Inmon, Claudia Imhoff, and Ryan Sousa (Wiley, 2000). Ralph Kimball s dimensional data warehouse architecture is described in 1 of The Data Warehouse Toolkit, Second Edition, by Ralph Kimball and Margy Ross (Wiley, 2002). It also discusses some of the myths about star schema covered in this chapter, as well as others. Although Kimball does not dedicate an entire book to his architecture, the basic philosophy permeates The Toolkit. Additional information can be found in The Data Warehouse Lifecycle Toolkit, Second Edition, by Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, and Bob Becker (Wiley, 2008). Because it is not a formal architecture, there is no book to describe the stand-alone data mart. Any discussion of implementation in a single subject area can be considered data-martcentric. Individual subject areas from The Data Warehouse Toolkit, implemented in the absence of an enterprise context, would fit the bill. Similarly, Data Warehouse Design Solutions by Chris Adamson and Mike Venerable (Wiley, 1998) provides dimensional designs for various warehouse subject areas.

So far, we have been talking about SQL Server architecture. This topic is somewhat theoretical, but is necessary in understanding how SQL Server works. Now, it s time to look at what you the database administrator or implementer work with daily. One of the most welcome enhancements to SQL Server 7 is easier administration. Database servers used to be the domain of specialized administrators who understood the architecture of the product and used archaic commands to interact with the machine. Realizing that not all systems administrators are also DBAs, Microsoft has greatly simplified the processes involved with designing, implementing, and managing a database solution. We have already talked about such features as dynamic file management, but that s just scratching the surface. SQL Server 7 includes many enhancements that improve manageability and decrease time spent in administration. Those who have worked with other RDBMSs will find the tools in SQL Server 7 to be intuitive and easier to use. Let s start with the heart of the administrative interface.

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