Discovering multidimensional model strengths and challenges
When multidimensional models were introduced, they were difficult for most report developers to wrap their heads around. They were not relational in nature and we had to think differently about structures. In order to create a good multidimensional model, we now need to understand dimensional modeling and denormalization (we dive into details on this in Chapter 3, Preparing Your Data for Multidimensional Models). However, the results were high performing, ad hoc-capable analytics databases that were simple for users to consume.
This section will focus on the strengths and challenges specific to multidimensional models. While not necessarily an exhaustive list, these strengths and challenges will influence your model choice. These are not listed in any particular order because they may apply differently to your specific technical or business needs.
Strengths of the multidimensional model
In this section, we will discuss...