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RamVegiraju
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databricks-metric-views

Define standardized YAML-based metric views for governed business KPIs.

4|4|Updated Jan 5, 2026
Search Tags:#metrics#yaml#kpis#databricks#unity-catalog#business-intelligence#metric-views
One-click install
npx skills add https://github.com/RamVegiraju/databricks-samples --skill databricks-metric-views-ramvegiraju
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-metric-views
Source: https://github.com/RamVegiraju/databricks-samples/tree/main/.claude/skills/databricks-metric-views
Command: npx skills add https://github.com/RamVegiraju/databricks-samples --skill databricks-metric-views-ramvegiraju

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Unity Catalog metric views define reusable, governed business metrics in YAML to ensure consistent definitions across teams and tools, reducing ad-hoc metric creation and version drift.

Core Features & Use Cases

  • Declarative metric definitions: Dimensions and measures are defined in YAML to enforce standard naming and aggregation rules.
  • Supports complex schemas: Joins (star/snowflake), window measures, and optional materialization for pre-computed metrics.
  • Cross-workspace governance: Enables sharing KPI definitions across dashboards, Genie, and SQL queries with consistent semantics.

Quick Start

Create a metric view using YAML to model orders data and expose measures via 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 governed metrics in Databricks to ensure consistent KPI definitions across teams?▼

Databricks metric views allow you to define governed metrics in YAML, ensuring standardized naming and aggregation rules across teams. This prevents version drift and ad-hoc metric creation in cross-workspace reporting.

Can I use YAML metric views to handle complex schemas with joins and window measures?▼

Yes, YAML metric views support complex schemas including star and snowflake joins, window measures, and optional materialization for pre-computing metrics, enabling robust business intelligence reporting.

What is the best way to share standardized business metrics across Databricks dashboards and SQL queries?▼

The best way to share standardized metrics is using Unity Catalog metric views, which declaratively define dimensions and measures in YAML to enforce consistent semantics across dashboards, Genie, and SQL queries.

Do I need a specific DBR version to create metric views with YAML in Unity Catalog?▼

Yes, creating YAML-based metric view definitions requires version 1.1 for DBR 17.2 or later. This ensures full support for dimensions, measures, joins, and optional materialization within Unity Catalog.

How does optional materialization work for pre-computing business KPIs in Databricks?▼

Optional materialization in YAML metric views allows you to pre-compute business KPIs like revenue and orders. This optimizes query performance for cross-workspace reporting and dashboards while maintaining governed definitions.

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