I am a founder of Kadre.io, an analytics consulting company. We focus primarily on companies looking to be small and scrappy, often setting up their data functions for the first time.
What I’ve found is that semantic layers, historically LookMl, have massive returns on being able to do more with less. If you set up a semantic layer from day one, you are able to empower non-sql users to be able to find correlated insights, set up complex dashboards, and generally self serve in a way that a SQL + Data Dictionary path doesn’t allow.
To me LookML is *near* the gold standard of what a semantic layer needs to be, except that’s locked away in a proprietary format and runtime. The future semantic layer needs to be open source. Currently DBT’s “semantic layer” is not a semantic layer, that’s just branding. MetricFlow is closer, and Lightdash’s “special flavor” of DBT is closer.
My talk is intended to
1. Put forward a definition of a semantic layer
2. Survey the different options out then, and their implementations
3. Show what a good open source interoperable semantic layer could do for us
Talks and Events
Practical Applications of The Semantic Layer
Semantic Layers are a way for us as data practitioners to codifier our knowledge of what the data means in a way that allows other people to self serve insights better. This presentation is a survey of the current state of semantic layers, including LookML, Atscale, DBT, MetricFlow and others. We’ll also look at how those semantic layers are being adopted by presentation layers, such as Lightdash, Hex, and others, and try and find what we should do today to be ready for tomorrow.
Track: Semantic Layer
- Data Modeling
- Data Architecture
- Semantic Layer
- Data Discoverability