Semantic Layers

Concepts covered: dmSemanticLayer

Define Metrics Once, Use Everywhere A semantic layer is a logical layer that defines business metrics in one place and makes them available to every consumer: dashboards, notebooks, APIs, and ad-hoc queries. Without one, every team defines 'revenue' slightly differently. Marketing counts refunds. Sales does not. Finance uses a different exchange rate. The numbers never match. The semantic layer sits between the physical tables and the consumers. It defines: what 'revenue' means (SUM(amount) WHERE status != 'refunded'), what dimensions are valid for grouping, and what filters are available. Every consumer uses the same definitions. Tools and Approaches The semantic layer solves the 'which number is right?' problem. When the CEO asks for revenue and gets three different answers from three te

About This Interactive Section

This section is part of the Design Patterns lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

How DataDriven Lessons Work

DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.