Technology Radar
Semantic layer is a data architecture technique that introduces a shared business logic layer between raw data stores and consuming applications, including business intelligence (BI) tools, AI agents and APIs. It centralizes metric definitions, joins, access rules and business terminology so consumers have shared definitions. The concept predates the modern data stack but has seen renewed interest with code-first approaches such as metrics stores.
Without a semantic layer, business logic scatters across ad-hoc warehouse tables, dashboards, and downstream applications, while metric definitions quietly diverge—particularly problematic when used to support business decisions. Our teams have seen this become more acute with agentic AI: using LLMs to perform naive text-to-SQL translations will frequently produce incorrect results, especially when business rules, such as revenue recognition, live outside the schema. Cloud platforms are now embedding semantic layers directly: Snowflake calls it Semantic Views and Databricks calls it Metric Views. Standalone tools such as dbt MetricFlow and Cube provide a portable layer across systems. The recent release of Open Semantic Interchange (OSI) v1.0, backed by multiple vendors, signals growing standardization and interoperability across analytics, AI, and BI platforms.
The main cost is upfront data modeling investment. Teams should start with a single domain rather than attempting an enterprise-wide rollout, as broad deployments often leave legacy reports running in parallel with the new layer, reintroducing inconsistent definitions.
