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Unit 3: Dimensional Data Warehouse

needs. This unit on data warehousing dimensions explains the importance of dimensions and Notes
dimension granularity and stresses the importance of flattening hierarchies—with the goal
being to make data more accessible and useful to users. It also focuses on fact and dimension
table.

3.1 Dimensional Model

Dimensional model comprises of a fact table and numerous dimensional tables and is used for
assessing summarized data. Since Business Intelligence reports are used in assessing the facts
(aggregates) across various dimensions, dimensional data modelling prefer the modelling
technique in a BI environment.
Facts are normally calculated data like dollars’ worth or Sales or income. They correspond to the
aim of a conclusion support analysis.
Dimensions define the axis of enquiry of a fact.

Example: For example, Product, Region and Time are the axes of enquiry of the Sales
detail.
One such enquiry could be a scenario where the user might require to see the Sales (in dollars)
for a specific item in a market over a specific time span of time. In this case, we are calculating the
fact (Sales) over three dimensions (Product, Region and Time). Thus we can say that dimensions
give different views of the facts. They give structure to the otherwise unstructured facts.
It typically contains the attributes for the SQL answer set. Figure 3.1 shows an example of
dimensional model.

Figure 3.1: Example of Dimensional Model

Source: www.oedewaldt.com/movies/dimensional%20modeling.pptý

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