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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.
Examples of fact data include sales price, sale quantity, and time, distance, speed and weight measurements. Related dimension attribute examples include product models, product colors, product sizes, geographic locations, and salesperson names. A star schema that has many dimensions is sometimes called a centipede schema. [4]
The common warehouse metamodel (CWM) defines a specification for modeling metadata for relational, non-relational, multi-dimensional, and most other objects found in a data warehousing environment. The specification is released and owned by the Object Management Group , which also claims a trademark in the use of "CWM".
In many Type 2 and Type 6 SCD implementations, the surrogate key from the dimension is put into the fact table in place of the natural key when the fact data is loaded into the data repository. [1] The surrogate key is selected for a given fact record based on its effective date and the Start_Date and End_Date from the dimension table.
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.
A cross-dimensional attribute is a dimensional or descriptive attribute whose value is defined by the combination of two or more dimensional attributes, possibly belonging to different hierarchies. For example, if a product value added tax (VAT) depends both on the product category and on the country where the product is sold, you can use a ...
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 ...
Data vault is designed to avoid or minimize the impact of those issues, by moving them to areas of the data warehouse that are outside the historical storage area (cleansing is done in the data marts) and by separating the structural items (business keys and the associations between the business keys) from the descriptive attributes.