Having considered the types of changes to dimension attributes and the ways to handle the dimension changes in the data warehouse, let us now turn our attention to a few other important issues about dimensions. One issue relates to dimension tables that are very wide and very deep.

In our earlier discussion, we had assumed that dimension attributes do not change too rapidly. If the change is a Type 2 change, you know that you have to create another row with the new value of the attribute. If the value of the attribute changes again, then you create another row with the newer value. What if the value changes too many times or too rapidly? Such a dimension is no longer a slowly changing dimension. What must you do about a not-so-slowly-changing dimension? We will complete our discussion of dimensions by considering such relevant issues.

11.3.1. Large Dimensions

You may consider a dimension large based on two factors. A large dimension is very deep; that is, the dimension has a very large number of rows. A large dimension may also be very wide; that is, the dimension may have a large number of attributes. In either case, you may declare the dimension as large. There are special considerations for large dimensions. You may have to attend to populating large-dimension tables in a special way. You may want to separate out some minidimensions from a large dimension. We will take a simple STAR schema designed for order analysis. Assume this to be the schema ...

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