Summary
In this chapter, we explored the primary processes of designing and developing robust semantic models in Power BI Desktop. Common semantic modeling challenges, including multiple grains and many-to-many relationships, were shown to be handled relatively easily with standard Power BI features. In addition, examples were provided for adding business logic and definitions developed using the DAX language. Also, use cases for increasing the value and sustainability of models via metadata settings and advanced features were explored. Finally, more advanced topics of column-level security and the use of aggregation and hybrid tables were explained.
Now that we understand how to build semantic models, we next turn our attention to authoring Power BI reports.
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