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databricks-metric-views

Create governed KPI metric views in Databricks Unity Catalog using YAML.

38|12|Updated Apr 24, 2026
Search Tags:#sql#data-governance#databricks#unity-catalog#kpi#business-metrics#metric-views
One-click install
npx skills add https://github.com/databrickslabs/coding-agents-databricks-apps --skill databricks-metric-views-databrickslabs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-metric-views
Source: https://github.com/databrickslabs/coding-agents-databricks-apps/tree/main/.claude/skills/databricks-metric-views
Command: npx skills add https://github.com/databrickslabs/coding-agents-databricks-apps --skill databricks-metric-views-databrickslabs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the widespread issue of inconsistent business metric definitions across teams and tools, which causes misaligned reporting, conflicting KPI values, and wasted time reconciling differences between dashboards, ad-hoc queries, and analytics platforms.

Core Features & Use Cases

  • Standardized Governed KPIs: Define reusable business metrics (revenue, order counts, conversion rates) in YAML that work natively across Databricks AI/BI Dashboards, Genie, and SQL queries.
  • Complex Aggregation Support: Build metrics with ratios, filtered measures, window functions (moving averages, running totals, year-to-date), and star or snowflake schema joins for multi-table data models.
  • Performance Optimization: Enable built-in materialization to pre-compute common metric aggregations, reducing query latency for frequently accessed KPIs.
  • Use Case: A sales operations team can define a single "Total Revenue" metric that is consistent across all sales dashboards, Genie natural language queries, and ad-hoc SQL reports, eliminating discrepancies from different calculation methods.

Quick Start

Use the databricks-metric-views skill to create a governed orders KPI metric view with monthly dimensions and total revenue, order count measures from your Databricks sales orders table.

Frequently Asked Questions about databricks-metric-views

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

FAQPage Schema
How do I standardize business metrics in Databricks to ensure consistent KPIs across dashboards?▼

You can standardize business metrics in Databricks by defining centralized, governed KPIs in Unity Catalog. This ensures consistent revenue tracking and order analytics calculations across all connected tools and SQL queries.

How does YAML-based metric definition work for Databricks Unity Catalog?▼

YAML-based metric definition allows you to specify governed business metrics for Unity Catalog. You define reusable KPIs, including complex aggregations and multi-table joins, which then work natively across Databricks AI/BI Dashboards and Genie.

Can I use complex aggregations like window functions and filtered measures in Databricks metric views?▼

Yes, Databricks metric views support complex aggregations including ratios, filtered measures, window functions for moving averages, and multi-table star or snowflake schema joins for comprehensive business metric calculations.

What is the best way to reduce query latency for frequently accessed KPIs in Databricks?▼

The best way to reduce query latency for frequently accessed KPIs in Databricks is to enable built-in materialization for metric views. This pre-computes common metric aggregations, significantly speeding up dashboard and SQL query performance.

Does this metric view approach work with Databricks Genie and SQL Editor?▼

Yes, governed metric views integrate natively with Databricks Genie, SQL Editor, and AI/BI Dashboards. This allows natural language queries and ad-hoc SQL reports to utilize the exact same centralized business metric definitions.

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