Expert Cube Development with Microsoft SQL Server 2008

We have two choices:
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Chapter 1
Create a very simple dimension for each of these attributes. This will lead
to rapid growth in the number of dimensions in the solution, something the
users will not like because it makes the cube harder to use.
Merge all these attributes in a so-called "Junk dimension". A junk dimension
is simply a dimension that merges together attributes that do not belong
anywhere else and share the characteristic of having only a few distinct
values each.
The main reasons for the use of a junk dimension are:
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If we join several small dimensions into a single junk dimension, we will
reduce the number of fields in the fact table. For a fact table of several million
rows this can represent a significant reduction in the amount of space used
and the time needed for cube processing.
Reducing the number of dimensions will mean Analysis Services performs
better during the aggregation design process and during querying, thereby
improving the end user experience.
The end user will never like a cube with 30 or more dimensions: it will be
difficult to use and to navigate. Reducing the number of dimensions will
make the cube less intimidating.
However, there is one big disadvantage in using a junk dimension: whenever we join
attributes together into a junk dimension, we are clearly stating that these attributes
will never have the rank of a fully-fledged dimension. If we ever change our mind
and need to break one of these attributes out into a dimension on its own we will not
only have to change the cube design, but we will also have to reprocess the entire
cube and run the risk that any queries and reports the users have already created
will become invalid.
Degenerate dimensions
Degenerate dimensions are created when we have columns on the fact table that
we want to use for analysis but which do not relate to any existing dimension.
Degenerate dimensions often have almost the same cardinality as the fact table;
a typical example is the transaction number for a point of sale data mart. The
transaction number may be useful for several reasons, for example to calculate
a "total sold in one transaction" measure.
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