databricks-metric-views

Define and manage Databricks Unity Catalog metric views using YAML.

Updated May 20, 2026

Search Tags:#yaml #databricks #measures #unity catalog #metric views #kpi modeling #governed analytics

Ready to run

npx skills add https://github.com/ice-droid-99/BI-Dashboard-Generation-Agent-in-Databricks- --skill databricks-metric-views-ice-droid-99

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: databricks-metric-views
Source: https://github.com/ice-droid-99/BI-Dashboard-Generation-Agent-in-Databricks-/tree/main/.gemini/skills/databricks-metric-views
Command: npx skills add https://github.com/ice-droid-99/BI-Dashboard-Generation-Agent-in-Databricks- --skill databricks-metric-views-ice-droid-99

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you define and maintain standardized Databricks Unity Catalog metric views so teams can share consistent KPI logic without duplicating fragile SQL across dashboards and tools.

Core Features & Use Cases

Create reusable business metrics in YAML that separate dimension logic from measure aggregations.
Model complex KPIs safely using filtered measures, ratio/per-unit measures, and window measures for trends and period-over-period calculations.
Govern metric definitions for consistent querying with STAR/SNOWFLAKE-style joins and permission-driven access.

Quick Start

Use the databricks-metric-views skill to generate a YAML metric view definition from your source table and then create or replace the governed metric view in Unity Catalog.

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 KPIs in Databricks Unity Catalog using YAML?▼

You define reusable KPIs in Databricks Unity Catalog by creating YAML metric view definitions that separate dimension logic from measure aggregations. This approach provides governed, standardized datasets for consistent querying across dashboards and AI/BI Genie.

What is the best way to standardize business metrics for Databricks dashboards?▼

The best way to standardize business metrics for Databricks dashboards is to use Unity Catalog metric views. They centralize KPI logic in YAML, preventing duplicated SQL and ensuring consistent filtered measures, ratios, and windowed metrics across reporting tools.

Can I model period-over-period calculations with Unity Catalog metric views?▼

Yes, you can model period-over-period calculations using window measures within Unity Catalog metric views. The YAML definitions support complex KPI modeling including filtered measures, ratio measures, and window functions for trend analysis.

Does Databricks metric view YAML support star and snowflake schema joins?▼

Databricks metric view YAML supports both STAR and SNOWFLAKE-style joins to structure your governed metric definitions. This allows you to combine multiple source tables while maintaining permission-driven access to standardized KPI datasets.

Do I need DBR 17.2 to create governed metric views in Unity Catalog?▼

Yes, you need DBR 17.2 or higher with YAML v1.1 compatibility to create governed metric views in Unity Catalog. This runtime version enables MEASURE() based querying semantics and lifecycle operations like create, alter, describe, query, drop, and grant.

How do I manage the lifecycle of a Unity Catalog metric view?▼

You manage the lifecycle of a Unity Catalog metric view using the manage_metric_views MCP tool. This tool executes lifecycle operations such as create, alter, describe, query, drop, and grant on your governed KPI datasets.