When designing a data warehouse, how you handle changes to dimensional data over time is the most important decision to make. It is rare that a dimension will remain static over time. For example, a customer may change their phone number or … Continue reading →
Junk dimensions are dimensions that contain miscellaneous data such as flags and indicators. When designing a data warehouse, you might come across a source system that has a bunch of yes/no indicator fields. If those fields needs to be tracked … Continue reading →
Dimensions are often recycled for multiple purposes within the same database. For instance, a “Date” dimension can be used for “Date of Sale”, as well as “Date of Delivery”, or “Date of Hire”. This is often referred to as a … Continue reading →
A conformed dimension is a dimension that has the same meaning to every fact with which it relates. Conformed dimensions allow facts and measures to be categorized and described in the same way across multiple facts and/or data marts, ensuring consistent … Continue reading →
A reference dimension occurs when the key column for the dimension is joined indirectly to the fact table through a key in another dimension table. This results in a snowflake schema design. The following figure shows one fact table named InternetSales, … Continue reading →
Degenerate dimensions, also called fact dimensions, are standard dimensions that are constructed from attribute columns in fact tables instead of from attribute columns in dimension tables. This is because useful dimensional data is sometimes stored in a fact table to … Continue reading →
In SSAS, data structures do not always conform to the snowflake or star schema model where one fact is associated with a single dimension member. For example, consider the example of financial transactions in accounts that can have one or … Continue reading →