Types of Dimensions in DWH

Types of Dimensions in Dimensional Data Modelling

👉 There are 9 types of Dimensions/metrics when dealing with Dimensional Data Modelling. They are given below:

🔹Conformed Dimension

🔹Outrigger Dimension

🔹Shrunken Dimension

🔹Role-Playing Dimension

🔹Dimension to Dimension Table

🔹Junk Dimension

🔹Degenerate Dimension

🔹Swappable Dimension

🔹Step Dimension

✅ Conformed Dimension

A Conformed Dimension is a type of Dimension that has the same meaning to all the Facts it relates to. This type of Dimension allows both Dimensions and Facts to be categorised across the Data Warehouse.

✅ Outrigger Dimension

An Outrigger Dimension is a type of Dimension that represents a connection between different Dimension Tables.

✅ Shrunken Dimension

A Shrunken Dimension is a perfect subset of a more general data entity. In this Dimension, the attributes that are common to both the subset and the general set are represented in the same manner.

✅ Role-Playing Dimension

A Role-Playing Dimension is a type of table that has multiple valid relationships between itself and various other tables. Common examples of Role-Playing Dimensions are time and customers. They can be utilised in areas where certain Facts do not share the same concepts.

✅ Dimension to Dimension Table

This type of table is a table in the Star Schema of a Data Warehouse. In a Star Schema, one Fact Table is surrounded by multiple Dimension Tables. Each Dimension corresponds to a single Dimension Table.

✅ Junk Dimension

A Junk Dimension is a type of Dimension that is used to combine 2 or more related low cardinality Facts into one Dimension. They are also used to reduce the Dimensions of Dimension Tables and the columns from Fact Tables.

✅ Degenerate Dimension

A Degenerate Dimension is also known as a Fact Dimension. They are standard Dimensions that are built from the attribute columns of Fact Tables. Sometimes data are stored in Fact Tables to avoid duplication.

✅ Swappable Dimension

A Swappable Dimension is a type of Dimension that has multiple similar versions of itself which can get swapped at query time. The structure of this Dimension is also different and it has fewer data when compared to the original Dimension. The input and output are also different for this Dimension.

✅ Step Dimension

This is a type of Dimension that explains where a particular step fits into the process. Each step is assigned a step number and how many steps are required by that step to complete the process.