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The process of dimensional modeling builds on a 4-step design method that helps to ensure the usability of the dimensional model and the use of the data warehouse. The basics in the design build on the actual business process which the data warehouse should cover. Therefore, the first step in the model is to describe the business process which ...
The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes related to fact data. Examples of fact data include sales price, sale quantity, and time, distance, speed and weight measurements. Related dimension attribute examples include ...
This method tracks historical data by creating multiple records for a given natural key in the dimensional tables with separate surrogate keys and/or different version numbers. Unlimited history is preserved for each insert. The natural key in these examples is the "Supplier_Code" of "ABC".
Without the guidance of a properly implemented data architecture design, common data operations might be implemented in different ways, rendering it difficult to understand and control the flow of data within such systems. This sort of fragmentation is undesirable due to the potential increased cost and the data disconnects involved.
For example, the Oracle FAQ defines a degenerate dimension as a "data dimension that is stored in the fact table rather than a separate dimension table. This eliminates the need to join to a dimension table. You can use the data in the degenerate dimension to limit or 'slice and dice' your fact table measures." [3]
A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product. A dimensional data element is similar to a categorical variable in statistics.
Database design is the organization of data according to a database model.The designer determines what data must be stored and how the data elements interrelate. With this information, they can begin to fit the data to the database model. [1]
The Kimball lifecycle is a methodology for developing data warehouses, and has been developed by Ralph Kimball and a variety of colleagues. The methodology "covers a sequence of high level tasks for the effective design, development and deployment" of a data warehouse or business intelligence system. [1]