The Rise of the Semantic Layer: Metrics On-The-Fly

A semantic layer is something we use every day. We build dashboards with yearly and monthly aggregations. We design dimensions for drilling down reports by region, product, or whatever metrics we are interested in. What has changed is that we no longer use a singular business intelligence tool; different teams use different visualizations (BI, notebooks, and embedded analytics).

Instead of re-creating siloed metrics in each app, we want to define them once, open in a version-controlled way and sync them into each visualization tool. That’s what the semantic layer does, primarily defined as YAML. Additionally, the semantic layer adds powerful layers such as APIs, caching, access control, data modeling, and metrics layer.

📝 The metrics layer is one component of a semantic layer. A limited metrics layer is usually built into a BI tool, translating its metrics to only that BI tool. The metrics and semantic layers we’ll talk about in this article support multiple BI tools, notebooks, and data apps.

ℹ️ The semantic layer, metrics layer, headless BI, and sometimes even metrics store are similar. Although the metrics layer is a part of the semantic layer, I will use them as synonyms and refer to all of them as a semantic layer, as the differences are minor and will lead to better understanding. If you like to dig…