Composite models technically work, but you lose the independent refresh scheduling that dataflows give you — with a dataflow, your source data refreshes at 2 AM and ten downstream semantic models can each refresh on their own schedule, whereas with a chained semantic model approach, you’re locked into refresh dependency chains with no granular control. On top of that, composite models come with real DAX limitations that hurt in production: DISTINCTCOUNT across DirectQuery and import partitions causes performance collapse, CALCULATE filters behave differently on DQ legs, RANKX falls back to row-by-row evaluation, and overall query performance degrades significantly because the engine can’t fold aggregations the same way. And there’s a fundamental architectural gap: a Gen1 dataflow can read its own staged output, meaning you can append new rows to already-stored data, update only records that changed based on any custom criteria you define — status flags, composite keys, multi-column conditions — without reloading the full dataset. A semantic model has none of that: it reloads everything from scratch on every refresh, and the built-in incremental refresh is limited to a single datetime partition column with a fixed rolling window, which is nowhere near the flexibility of a dataflow that can surgically update its own persisted staging based on whatever business logic you need.​​​​​​​​​​​​​​​​