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Dimension types


Rapidly Changing DimensionsA dimension is considered to be a rapidly changing if one or more of its attributes changes frequently in many rows. For a rapidly changing dimension, the dimension table can grow very large from the application of numerous type 2 changes.

Slowly Changing Dimensions
 Attributes of a dimension that would undergo changes over time. It depends on the business requirement whether particular attribute history of changes should be preserved in the data warehouse. This is called a Slowly Changing Attribute and a dimension containing such an attribute is called a Slowly Changing Dimension.

Conforming Dimension
A dimension table may be used in multiple places if the data warehouse contains multiple fact tables or contributes data to data marts.
For example,A dimension such as customer, time, or product that is used in multiple schemas is called a conforming dimension if all copies of the dimension are the same
Use of Conforming Dimensions in Multiple Facts: Multiple fact tables are used in data warehouses that address multiple business functions, such as sales, inventory, and finance. Each business function will typically have its own schema that contains a fact table, several conforming dimension tables, and some dimension tables unique to the specific business function

Degenerate dimension
A degenerate dimension is a dimension that is stored in the fact table rather than the dimension table. It eliminates the need to join to a Dimension table.
Example:
if you have a dimension that only has Order Number and Order Line Number, you would have a 1:1 relationship with the Fact table. Do you want to have two tables with a billion rows or one table with a billion rows. Therefore, this would be a degenerate dimension and Order Number and Order Line Number would be stored in the Fact table.

Junk dimension
Junk dimensions are dimensions that contain miscellaneous data (like flags and indicators) that do not fit in the base dimension table

Role Playing Dimensions
A role-playing dimension is one where the same dimension key — along with its associated attributes — can be joined to more than one foreign key in the fact table. For example, a fact table may include foreign keys for both Ship Date and Delivery Date. But the same date dimension attributes apply to each foreign key, so you can join the same dimension table to both foreign keys. Here the date dimension is taking multiple roles to map ship date as well as delivery date, and hence the name of Role Playing dimension.

Shrunken Dimensions
A shrunken dimension is a subset of another dimension. For example, the Orders fact table may include a foreign key for Product, but the Target fact table may include a foreign key only for ProductCategory, which is in the Product table, but much less granular. Creating a smaller dimension table, with ProductCategory as its primary key, is one way of dealing with this situation of heterogeneous grain. If the Product dimension is snowflaked, there is probably already a separate table for ProductCategory, which can serve as the Shrunken Dimension.

Static Dimensions
Static dimensions are not extracted from the original data source, but are created within the context of the data warehouse. A static dimension can be loaded manually — for example with Status codes — or it can be generated by a procedure, such as a Date or Time dimension.

Inferred Dimensions
Inferred Dimension is a dummy dimension record inserted in dimension table to handle transaction record processing when there is no matching dimension data found for transaction data. This should technically be called an inferred member, but is often called an inferred dimension.
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