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One of the most important applications for the earliest database management systems was production planning for manufacturing companies. If an automobile manufacturer decided to produce 10,000 units of one car model and 5,000 units of another model, it needed to know how many parts to order from its suppliers. To answer the question, the product (a car) had to be decomposed into assemblies (engine, body, chassis), which were decomposed into subassemblies (valves, cylinders, spark plugs), and then into subsubassemblies, and so on. Handling this list of parts, known as a bill of materials, was a job tailor-made for computers. The bill of materials for a product has a natural hierarchical structure. To store this data,

design on a large scale; a Corporate Information Factory can do so optionally; a stand-alone data mart leverages its benefits only within a subject area. Regardless of the level of emphasis, however, dimensional design must always precede the design of ETL programs and reports. This means it is usually conducted prior to the typical design stage of an implementation project, although exactly when and how can vary greatly.

The process is similar if you want to modify the settings after the database has been created. To specify data file options when creating a database, use the following syntax: CREATE DATABASE database_name ON ( [ NAME = logical_filename, ] FILENAME = os_filename [, SIZE = size] [, MAXSIZE = { max_size | UNLIMITED } ] [, FILEGROWTH = growth_increment] ) [,...n] [ LOG ON { <filespec> [,...n]} ] <filespec> ::= ( [ NAME = logical_filename, ] FILENAME = os_filename [, SIZE = size] [, MAXSIZE = { max_size | UNLIMITED } ] [, FILEGROWTH = growth_increment] ) [,...n] For example, the following command creates a database that resides on a single 10MB data file that is set to automatically grow by 10 percent until it reaches 100MB:

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Fundamentally, a dimensional model deals with the measurement of business processes. It describes how a business process is evaluated, and it can be used to frame questions about the process. In this respect, it speaks clearly to the business users of the data warehouse. A dimensional model also has technical implications. Its definition determines the data sources that must be integrated, how information must be cleansed or standardized, and what queries or reports can be built. In this respect, it speaks clearly to the developers of the data warehouse. These business and technical characteristics of the dimensional model make it an ideal focal point for managing data warehouse strategy. A dimensional model can serve as the basis for a shared understanding of project priorities, scope, and progress. From a business perspective, it imparts a clear understanding of functional capability; from an IT perspective, it supports a clear understanding of the level of effort required for implementation. Data warehouse architectures place varying emphasis on the strategic value of dimensional design. It is most strongly emphasized in Kimball s dimensional data warehouse architecture, which uses conformed dimensions as a central organizing feature. In Inmon s Corporate Information Factory architecture, this emphasis is often reduced, though similar benefits can be leveraged. A stand-alone data mart places the least amount of strategic emphasis on the dimensional design.

CREATE DATABASE TestDB ON ( Name = 'TestFile', FileName = 'c:\mssql7\data\testfile.mdf', Size = 10MB, Maxsize = 100MB, Filegrowth = 10%)

Kimball s dimensional data warehouse architecture, also known as the bus architecture, places a strong strategic emphasis on the principles of dimensional design. This emphasis stretches far beyond the design of database tables. As a framework for understanding process measurement, the dimensional model is used to capture analytic requirements from across the enterprise; as the basis for project scope, it is used to establish development priorities and resource requirements. Most importantly, the development of an enterprise dimensional model enables the incremental implementation of subject-area applications, while avoiding the risk of incompatibilities, or stove pipes. This benefit is made possible by planning implementations around a set of conformed dimensions. As you learned in 5, conformed dimensions serve as the basis for comparing data from different subject areas, through a process called drilling across. In the dimensional data warehouse architecture, dimensional design is the basis for an logical repository of atomic data that has been drawn from throughout the enterprise. As you learned in 2, a subject area within this repository is called a data mart. It may be queried directly, or serve as the basis for a second level of dimensional structures focused on the needs of particular departments or groups.

the hierarchical data model, illustrated in Figure 4-2, was developed. In this model, each record in the database represented a specific part. The records had parent/child relationships, linking each part to its subpart, and so on.