In data modeling theory regarding fact tables, distinct grain and event definitions preclude direct relationships or aggregation operations between facts; instead, integration requires a shared dimension to act as a single point of truth for both table references. This mechanism ensures dimensional completeness while preventing cardinality errors in reporting metrics derived from different levels of detail. The domain is analytical data modeling within business intelligence, specifically addressing the structural rules necessary for accurate metric comparison across heterogeneous fact structures without inducing duplicate values or logical inconsistencies.
In data modeling theory regarding fact tables, distinct grain and event definitions preclude direct relationships or aggregation operations between facts; instead, integration requires a shared dimen…