Question: 22) DWH Type of Dimension Tables in dataware Housing

 

Types of Dimensions

 

Dimension

A dimension table typically has two types of columns, primary keys to fact tables and textual\descriptive data.

Eg: Time, Customer

 

 

Types of Dimensions

 

1.          Slowly Changing Dimensions

2.          Rapidly Changing Dimensions

3.          Junk Dimensions

4.          Inferred Dimensions

5.          Conformed Dimensions

6.          Degenerate Dimensions

7.          Role Playing Dimensions

8.          Shrunken Dimensions

9.          Static Dimensions 

 

Slowly Changing Dimensions

Dimension attributes that change slowly over a period of time rather than changing regularly is grouped as SCDs.  Attributes like name, address can change but not too often.

These attributes can change over a period of time and that will get combined as a slowly changing dimension. Consider an example where a person is changing from one city to another. Now there are 3 ways to change the address;

Type 1  is to over write the old value, Type 2 is to add a new row and Type 3 is to create a new column.

Type 1

The advantage of type 1 is that it is very easy to follow and it results  in huge space savings and hence cost savings. The disadvantage is that no history is maintained.

Type 2

The advantage of type 2 is that the complete history is maintained. The only disadvantage lies in the huge space allocation because the entire history right from the start has to be maintained.

 

Type 3

The best approach could be to add a new column where you add two new columns. In this case keeping a tracking of the history becomes very easy.

 

Rapidly Changing Dimensions[fast changing dim]

A dimension attribute that changes frequently is a rapidly changing attribute. Fast changing dimensions are those dimensions if one or more of its attributes changes frequently and in many rows. A fast changing dimension can grow very large if we use the Type-2 approach to track numerous changes. These dimensions some time called rapidly changing dimensions.

Examples of fast changing dimensions are
Age
Income
Test score
Rating
Credit history score
Customer account
status
Weight

 

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.

 

Inferred Dimensions (late coming dimension)

 

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.

 

For example, there are two fact tables. Fact table 1 is to determine the number of products sold by geography. This table will calculate just the number of products by geography and fact table 2 will determine the revenue generated by customer. Both are dependent on the product which contains product Id, name and source.

 

There is the geography dimension and customer dimension which are being shared by two fact tables. The revenue fact gives the revenue generated by both the geography and the customer, while the product units fact gives number of units sold in the geography to a customer.

 

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.

 

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.