databricks-metric-views

Define reusable business metrics in YAML for Unity Catalog metric views.

27|9|Updated Feb 3, 2026

Search Tags:#metrics #yaml #data-modeling #kpis #databricks #unity-catalog

Ready to run

npx skills add https://github.com/datasciencemonkey/coding-agents-databricks-apps --skill databricks-metric-views

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: databricks-metric-views
Source: https://github.com/datasciencemonkey/coding-agents-databricks-apps/tree/main/.claude/skills/databricks-metric-views
Command: npx skills add https://github.com/datasciencemonkey/coding-agents-databricks-apps --skill databricks-metric-views

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Unity Catalog metric views define reusable, governed business metrics in YAML, separating measure definitions from dimension groupings to enable flexible, scalable analytics.

Core Features & Use Cases

Define standardized metrics (dimensions and measures) that can be reused across dashboards, Genie, and SQL queries.
Support complex patterns like joins, materialization, and window measures to model star/snowflake schemas and time-based calculations.
Promote governance by centralizing metric definitions and enabling consistent KPI calculations across teams.

Quick Start

Create a YAML metric view describing version, source, dimensions, and measures, then query it with MEASURE() in SQL.

Frequently Asked Questions about databricks-metric-views

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I define reusable business metrics in Databricks using YAML?▼

You can define reusable business metrics in Databricks by creating YAML metric views that specify version, source, dimensions, and measures, then querying them with the MEASURE() function in SQL to standardize KPI calculations.

How does Unity Catalog metric views standardize KPI calculations across dashboards?▼

Unity Catalog metric views standardize KPI calculations by centralizing metric definitions in YAML, separating measures from dimension groupings to ensure consistent query-time aggregation across dashboards, Genie, and SQL queries.

Can I model star schemas and time-based calculations with Databricks metric views?▼

Yes, Databricks metric views support complex modeling patterns like joins, materialization, and window measures, allowing you to model star or snowflake schemas and perform time-based calculations within your YAML definitions.

What is the best way to govern business metrics in a Databricks data warehouse?▼

The best way to govern business metrics in Databricks is to centralize their definitions using Unity Catalog metric views, which enforces a YAML schema for dimensions and measures to ensure safe, consistent query-time aggregation.

Do I need Unity Catalog to use YAML-driven metric views in Databricks?▼

Yes, you need Unity Catalog, as the YAML-driven metric views leverage Unity Catalog to define and govern reusable business metrics, applying the schema to standardize calculations and enable safe query-time aggregation.

What schema elements are required when creating a YAML metric view in Databricks?▼

Creating a YAML metric view in Databricks requires a schema with version, source, dimensions, and measures, while also supporting optional configurations for joins and materialization to handle complex data modeling.