Intro to Count Metrics

Create a governed data model using our powerful semantic layer.

Count Metrics is Count’s semantic layer, providing a trusted source for your data logic, from business metrics to complex SQL and repetitive joins, by defining them once. Consistently and centrally.

The Count Metrics advantage

Bring clarity, speed, and consistency to your analytics workflow. Here’s how Count Metrics transforms the way your team works with data:

• Democratized complex SQL code write complex queries once, reuse everywhere

• Reduced errors and inconsistencies align teams on shared definitions like revenue or retention

• Faster, clearer insights for decision makers Empower your explorer community to produce their own low-code analysis on pre-built Count Metrics datasets

• Less time wrangling and more time analyzing Standardize repetitive SQL logic and free up more time to focus on insights

• Unlimited exploration without burdening source systems via customizable caching analysts can explore data cached on our server without overloading your connected database

• Lower canvas load times via customizable caching, canvas load times are significantly reduced

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.

What this doesn't mean

Myth	Reality
❌ You can’t write SQL anymore	✅ You still write SQL, you just write the complex, or reusable parts once and reuse them everywhere.
❌ You’ll lose flexibility	✅ You can still write custom queries when needed. Count Metrics standardizes the boring bits so you can focus on deep analysis.
❌ It’s only for data engineers	✅ In Count, designing and evolving the semantic layer can be a collaborative activity. Your semantic layer is a shared foundation - not an owned artifact.

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.

• 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.

• 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.

• 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.