Inventory the number of downstream BI tools and data consumers in your environment; a semantic layer is typically preferable when metrics must be consistent across three or more tools because business logic is defined once and served to all consumers
Assess your SLA and audit requirements: data marts with pre-materialized tables offer predictable query latency and point-in-time snapshots suitable for regulated reporting, while semantic layers compute on-the-fly and may have variable latency depending on caching
Evaluate change velocity: if business metric definitions change frequently, a semantic layer reduces the blast radius of a definition change to one place; with data marts, each mart must be individually updated and redeployed
Consider a hybrid architecture for high-priority metrics: define metrics centrally in the semantic layer and selectively materialize mart tables for dashboards with strict latency SLAs or audit snapshot requirements, using the semantic layer as the source of truth for the mart's transformation logic
Document the decision with the specific drivers (tool count, SLA, change frequency, infrastructure cost) so that the architecture can be revisited as requirements evolve
Known gotchas
Semantic layers add an infrastructure component that must be operated, monitored, and maintained; the operational overhead is often underestimated when comparing them to data marts that leverage existing warehouse infrastructure
Pre-built data marts with denormalized schemas can outperform semantic layer live queries by orders of magnitude for large-scale aggregations; benchmark both approaches on representative production query patterns before committing to one
A semantic layer does not eliminate the need for clean, well-modeled source tables; it abstracts the join and aggregation logic but still depends on accurate upstream data — poor data quality propagates through the semantic layer just as it does through a mart
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