
Modern data teams face growing complexity: multiple data sources, various BI tools, and ever-increasing self-service analytics. Organizations must maintain consistency across their metrics while making data accessible to business users. This is where a semantic layer shines: It provides a single source of truth for business metrics while abstracting away the underlying complexity.
In Part 1, we explored the semantic layer through the lens of MVC, discovering how it acts as both a controller for data access and a model for business metrics. While this architectural pattern helps us understand its role, the real value of a semantic layer comes from its everyday use.
This article examines how semantic layers fit into modern data architectures and their critical benefits, from API-driven access to enhanced governance, and why they’ve become essential in today’s data stack.
Continue reading the full article at Cube.dev →
The full article covers:
- How semantic layers evolved from traditional to modern data architectures
- The different types and core components of semantic layers
- Key benefits including unified data access, enhanced security, and improved performance
- Integration capabilities with various APIs including SQL, GraphQL, and Excel
Full article published at Cube.dev - written as part of my services