In order to develop a Power BI data model that can be easily enhanced or simplified without much effort, Consider a powerful construction that provides an equilibrium performance, elasticity, and ease. Observe the following cardinal rules:
Implement Star Schema Design: Construct the model by having optic-obtained fact tables located in the center and descriptive dimension-related tables around them. This eases the complexity of relationships and makes them less redundant, hence improving query performance. Extremely flat designs or highly normalized designs that have lots of relationships should be discouraged, as such structures reduce performance levels and increase the complexity of relationships.
Enhance Relationship Optimization: Apply single-direction relationships as much as possible where they are clearly understood and expected in order to improve performance levels. The use of relationships is discouraged at all costs, particularly where high cardinality columns are used due to the fact that they are very performance-threatening and lead to clearer filter contexts. When dealing with many-to-many scenarios that are prone to overshadow complex calculations, the use of bridging tables, which are designed to relate two facts, should be brought in.
Control Data Grain and Volume: The level of detail in the fact table must be appropriate for the requirement of the analysis. Either unnecessary levels are avoided, or if present, measures are taken to prevent such levels from inflating the dataset volume. Preprocess and aggregate the data with Power Query prior to putting money into the model. It is also advised to make use of incremental refreshes for bulk data so that only the new or edited parts are loaded into the model, making it more sustainable as the data increases.
If you follow all these guidelines, your Power BI data model will always be efficient, scalable, and open to change.
