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 :)
