...
Explore the possibility to Reduce the Number of Dimensions by:
Combining dimensions: At the dataset level, whenever possible, combine dimensions with a fewer number of attributes (1-3) into one dimension to reduce the number of dimensions. For example, some dimensions can be merged at the register file level to reduce the number of dimensions at the semantic model level.
Multiple hierarchies: Consider using multiple hierarchies if you need two types of time data, such as year-month-day and year-quarter-month-day, for different purposes. Or if you need two types of location data such as division-region-district-location and state-county-city-location.
Dimension merging: At the time of semantic model processing, the dimensions and combinations of different dimensions are pre-aggregated/materialized and stored on a disk. If the number of dimensions in a semantic model is too high, the size of materialization becomes large to accommodate existing architecture. This leads to high process time, increased size on disk, and higher read time while querying.
You can merge two or more dimensions related to the same or different transformation into a single dimension. The subset of facts to which these multiple transformations are related should be the same. This allows saving disk space, reduces process time to materialize semantic models, and improves query performance.
Revisit Distinct Count Measures
Explore the possibility to see if any Distinct count measures can be removed if not needed.
Or Accurate counts can be converted to Approximate Counts?
Explore the possibility of using Boundary Based Distinct Counts wherever possible for High Cardinalities.
Also, explore if Sum or Count functions can be used instead of Distinct Count to derive the same requirement.
...