What Is a Semantic Layer?
21 hours ago
- #semantic-layer
- #data-engineering
- #modern-data-stack
- The author expresses skepticism and humor about the sudden popularity of the term 'Semantic Layer' in data engineering.
- Different vendors (Databricks, DuckDB, Snowflake) provide varying definitions of a Semantic Layer, leading to confusion.
- Common themes in definitions include standardizing metrics, data governance, and transformations, but no clear consensus exists.
- Snowflake introduced 'semantic views' as a concrete implementation, while Databricks considers its entire product a Semantic Layer.
- Historical context shows the term 'Semantic Layer' emerged around 2015 (AtScale) and gained traction in the Modern Data Stack (MDS) by 2022–2024 (dbt Labs, Cube).
- The Semantic Layer aims to solve age-old problems like single-source truth, governance, and consistent metrics—issues still plaguing organizations.
- The author critiques the need for additional SaaS tools, arguing existing platforms (Databricks, Snowflake) can solve these problems if used properly.
- Ultimately, the Semantic Layer is seen as rebranded data modeling—useful but not revolutionary—and success depends on stakeholder alignment and engineering discipline.