Most analytics stacks model data into a curated gold layer and call the result a semantic layer. For a lot of workloads, that’s a perfectly good one. The business logic lives in version-controlled models, every consumer reads the same tables over SQL, and there’s no extra product to run. The honest position is that a well-modeled gold layer is a semantic layer for standardizing metrics, and reaching for a dedicated tool before you need one is over-engineering.
This post is about the point where that stops being true: when users need to compute a metric across arbitrary combinations of dimensions, chosen at query time, and some of those metrics are non-additive. We will first show, with real numbers, why pre-aggregated gold tables produce silently wrong results in that case. Then we will build a working solution on Amazon Redshift using the open-source dbt Semantic Layer (powered by MetricFlow), define average_order_value once, and prove it stays correct whether…
