databricks-metric-views

Define reusable business metrics in YAML for Databricks Runtime 17.2+.

Updated Sep 9, 2017

Search Tags:#analytics #yaml #joins #databricks #metric-views #governed-metrics

Ready to run

npx skills add https://github.com/mirakui/dotfiles --skill databricks-metric-views-mirakui

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Unity Catalog metric views enable teams to define and govern reusable business metrics in YAML, separating metric definitions from queries to ensure consistency across dashboards and tools.

Core Features & Use Cases

Define standardized metrics (revenue, KPI layers) and share across dashboards, Genie, and SQL workflows.
Support complex aggregations, window measures, and star/snowflake schema joins for flexible modeling.
Enable materialization and cross-team metric governance for faster, reliable analytics.

Quick Start

Create a metric view by defining dimensions and measures in YAML, then query with MEASURE() across the defined metrics.

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 YAML for Databricks analytics?▼

To define reusable business metrics in YAML, you specify dimensions and measures within a Unity Catalog metric view, separating metric definitions from queries to ensure consistency across Databricks dashboards and SQL workflows.

Can I use star or snowflake schema joins when building governed metrics?▼

Yes, you can build governed metrics using star or snowflake schema joins. The metric view definitions support optional joins to integrate data across complex schemas for flexible analytics modeling.

What is the best way to standardize revenue metrics across cross-team dashboards?▼

The best way to standardize revenue metrics across cross-team dashboards is to define them as governed metric views in YAML, enabling consistent application and centralized governance across teams.

Does Databricks metric view support complex aggregations and window measures?▼

Yes, Databricks metric views support complex aggregations and window measures. You can define these advanced analytical calculations directly in YAML alongside your dimensions for materialization.

Do I need a specific Databricks Runtime version to use YAML metric views?▼

Yes, you need Databricks Runtime 17.2 or higher to use YAML metric views. This environment requirement ensures full support for defining dimensions, measures, optional joins, and materialization.

How do I query defined metrics after creating a YAML metric view?▼

After creating a YAML metric view by defining dimensions and measures, you query the defined metrics using the MEASURE() function across your specified SQL workflows and dashboards.