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juanlamadrid20
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databricks-metric-views

Define and manage Unity Catalog metric views using declarative YAML specifications.

Updated Mar 12, 2026
Search Tags:#metrics#data modeling#yaml#kpis#databricks#unity catalog
One-click install
npx skills add https://github.com/juanlamadrid20/coda --skill databricks-metric-views-juanlamadrid20
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-metric-views
Source: https://github.com/juanlamadrid20/coda/tree/main/.claude/skills/databricks-metric-views
Command: npx skills add https://github.com/juanlamadrid20/coda --skill databricks-metric-views-juanlamadrid20

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables the definition, creation, querying, and management of standardized, governed business metrics directly within Unity Catalog using a declarative YAML format, simplifying KPI management and ensuring consistency across analytics tools.

Core Features & Use Cases

  • Define Reusable Metrics: Create metrics like revenue, conversion rates, or YTD calculations in YAML.
  • Govern Business Logic: Ensure consistent metric definitions across teams and tools.
  • Use Case: A finance team needs to track monthly recurring revenue (MRR) consistently. They define an MRR metric view in YAML, which can then be queried by dashboards, BI tools, and AI assistants, ensuring everyone uses the same calculation.

Quick Start

Use the databricks-metric-views skill to create a metric view named 'orders_metrics' in catalog.schema with dimensions and measures defined in YAML.

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 standardized business metrics in Unity Catalog using YAML?▼

You can define standardized business metrics in Unity Catalog by writing a declarative YAML specification that includes dimensions and measures. This approach governs KPIs and ensures consistent calculation logic across all analytics tools.

Can I create complex KPIs like ratios and window measures in Databricks metric views?▼

Yes, Databricks metric views support complex business KPIs including ratios, window measures, and star or snowflake schema joins. You can define these advanced calculations declaratively in YAML for standardized reporting.

What is the best way to govern business logic for consistent KPI reporting in Databricks?▼

The best way to govern business logic is using a declarative YAML format to create Unity Catalog metric views. This ensures everyone queries the same standardized metric definitions, preventing calculation discrepancies across BI tools and dashboards.

Does this approach to metric management support star and snowflake schema joins?▼

Yes, creating metric views through YAML supports both star and snowflake schema joins. This allows you to combine data from multiple tables while maintaining governed, standardized metric definitions for analytics.

How do I query a Unity Catalog metric view defined in YAML from a dashboard?▼

Once you define a metric view like 'orders_metrics' in YAML within a specific catalog and schema, it becomes queryable. Dashboards, BI tools, and AI assistants can query this standardized view to retrieve consistent KPI data.

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