Intro to Count Metrics

Create a governed data model using our powerful semantic layer.

Count Metrics is Count’s semantic layer, providing a centralized way to define, manage, and control access to key metrics. By standardizing metric definitions, it ensures consistency and accuracy across your organization while enabling stakeholder self-serve access to data. Designed for deep integration with the canvas, Count Metrics makes it easy for both technical and non-technical users to explore data and use metrics exactly where they are needed.

Common use cases

• Creating operational reports from a set of company defined metrics

• Allowing stakeholders to self serve

• Simplified reporting

• Quick data exploration

Concepts

The semantic layer consists of three types of entity: catalogs, datasets and views.

• Catalogs — the highest-level object within a semantic layer. Catalogs are self-contained entities that house various views and datasets, and can be used as project data sources (just like database connections). Each catalog is stored in a separate Git repository.

• Datasets — A collection of views and the information about the relationships between them. These make up the tables that users see when constructing queries from the semantic layer.

• Views — Selections of fields (measures and dimensions), along with assorted metadata. Views can be quickly initialised from database tables and canvas cells, and can be executed on remote databases, as well as in DuckDB and Python local to the user.