Explain the concept of Multi-dimensional Data Modeling and the different types…
Explain the concept of Multi-dimensional Data Modeling and the different types of measures used in Fact Tables.
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Multi-dimensional data modeling views data in the form of a data cube, which allows for the analysis of data across multiple dimensions such as time, item, and location. This model is composed of Fact Tables and Dimension Tables.
A Fact Table contains the quantitative measures or metrics of a business process (e.g., dollars_sold, units_sold) and foreign keys to the dimension tables. Measures in a fact table are classified into three types:
Fully Additive Measures: These can be summed across any and all dimensions in the fact table (e.g., sales amount).
Semi-Additive Measures: These can be summed across some dimensions but not all. For example, account balances are additive across the customer dimension but not across the time dimension.
Non-Additive Measures: These cannot be added across any dimension, usually because they are ratios or percentages.
A Fact-Less Fact Table is a specialized fact table that contains no measures, only foreign keys. It is used to record the occurrence of an event or the association between dimensions (e.g., tracking student attendance).