Bringing the Semantic Layer Into a Model-Driven, Business-Friendly Data Architecture

From universal data models to GenAI-powered self-service analytics — how to integrate dbt’s semantic layer approach into a metadata-first way of working.

Summary

The promise: A semantic layer lets us define metrics and business logic once, centrally, and expose them consistently to dashboards, AI, and data apps.
The challenge: Most teams already pursue model-driven engineering, business-friendly mappings, and metadata governance — how does a new “semantic layer” fit in?
This article: Shows how to embed dbt’s semantic layer concept (MetricFlow) into a modern, model-first, metadata-driven data platform and turn it into a foundation for GenAI-enabled analytics.

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Why semantic layers matter

The original article by Abhishek Gupta explains how dbt’s semantic layer centralizes KPI and metric definitions, enabling governed self-service and freeing BI tools from duplicating logic.

Our platforms already emphasize:

• Model-driven data engineering — code-as-models, automated diagram generation, text-based mappings
• Business-friendly…