Many ask themselves, «Why would I use a semantic layer? How to build one?». But a better question is: How many times have you implemented the same revenue calculation differently across your company's dashboards, reports, and apps? This is why semantic layers exist.
With an SL, your revenue KPI or other complex company measures are defined once in a single source of truth—no need to re-implement them over and over again. In my article, we'll look at the simplest possible SL, which uses a simple YAML file (semantics) and Python/Ibis (executing) & DuckDB.
Some Chapters and Insights: - When you DON'T need a Semantic Layer - Why use a semantic layer with the differentiation of «Datasets vs. Aggregations» - A practical example with DuckDB, Boring Semantic Layer (@hachej.bsky.social 👋), and Ibis. Building a DSL for our Metrics and KPIs. - SL FAQs
Think of the differentiation it this way: » dataset ≠ aggregations » table columns ≠ metrics » physical table ≠ logical definition If you find yourself needing the concepts on the right side, that's when you need a semantic layer, either for building into a BI tool or implemented separately.
I hope you enjoy. Happy to discuss further. Exciting times ahead for BI and for the semantic layer. PS: It's on the front page of Hackernews right now (30 position), but peaked yesterday evening :)
