Types of Dimension Tables in a Data Warehouse

Dimensions store the textual descriptions of the business. With help of dimension you can easily identify the measures.The different types of dimension tables are available as below:

Types of Dimension Tables

Slowly Changing Dimensions (SCD)

This is the popular dimension type. 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. Eg. Home Address doesnt change often, its a SCD attribute

Rapidly Changing Dimensions

A dimension attribute that changes frequently is a rapidly changing attribute. If you don't need to track the changes, the rapidly changing attribute is no problem, but if you do need to track the changes, using a standard slowly changing dimension technique can result in a huge inflation of the size of the dimension. One solution is to move the attribute to its own dimension, with a separate foreign key in the fact table. This new dimension is called a rapidly changing dimension. Eg. Body Temperature is a rapidly changing attribute

Junk Dimensions

A junk dimension is a single table with a combination of different and unrelated attributes to avoid having a large number of foreign keys in the fact table. Junk dimensions are often created to manage the foreign keys created by rapidly changing dimensions. For example, attributes such as flags, weights, BMI (body mass index) etc

Inferred Dimensions

While loading fact records, a dimension record may not yet be ready. One solution is to generate a surrogate key with null for all the other attributes. This should technically be called an inferred member, but is often called an inferred dimension

Conformed Dimensions

A dimension that is used in multiple locations is called a conformed dimension. A conformed dimension may be used with multiple fact tables in a single database, or across multiple data marts or data warehouses. Conformed dimension example would be Customer dimension, i.e. both marketing and sales department can use Customer dimension for their reporting purpose

Degenerate Dimensions

A degenerate dimension is when the dimension attribute is stored as part of fact table, and not in a separate dimension table. These are essentially dimension keys for which there are no other attributes. In a data warehouse, these are often used as the result of a drill through query to analyze the source of an aggregated number in a report. You can use these values to trace back to transactions in the OLTP system. For example, receipt number does not have dimension table associated with it. Such details are just for information purpose

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. For example, you can use a date dimension for “date of sale”, as well as “date of delivery”, or “date of hire”

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






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