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metric-views-patterns

Generate Databricks Metric Views YAML with schema validation and join patterns.

5|6|Updated Jan 26, 2026
Search Tags:#data modeling#yaml#semantic layer#databricks#genie#metric views
One-click install
npx skills add https://github.com/databricks-solutions/vibe-coding-workshop-template --skill metric-views-patterns
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: metric-views-patterns
Source: https://github.com/databricks-solutions/vibe-coding-workshop-template/tree/main/data_product_accelerator/skills/semantic-layer/01-metric-views-patterns
Command: npx skills add https://github.com/databricks-solutions/vibe-coding-workshop-template --skill metric-views-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pypdf, pdfplumber, pdf2image, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a standardized approach to creating Databricks Metric Views, ensuring consistency, discoverability, and optimal performance for AI/BI tools like Genie.

Core Features & Use Cases

  • Metric View Creation: Generates YAML definitions for Databricks Metric Views.
  • Schema Validation: Includes scripts to validate column references against source tables before deployment.
  • Join Patterns: Demonstrates how to implement direct and snowflake schema joins.
  • Use Case: When building a semantic layer for a data product, use this Skill to define key business metrics (e.g., Total Revenue, Active Users) in a way that Genie can easily understand and query using natural language.

Quick Start

Use the metric-views-patterns skill to create a new metric view YAML file for sales performance.

Frequently Asked Questions about metric-views-patterns

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

FAQPage Schema
How do I create a semantic layer metric view in YAML for Databricks Genie?▼

To create a semantic layer metric view for Genie, you define business metrics using YAML patterns that ensure schema validation and join implementation comply with the v1.1 specification for natural language queries.

What is the best way to standardize Databricks metric views for AI/BI tools?▼

Standardizing Databricks metric views involves using YAML definitions to enforce schema validation and semantic metadata consistency, ensuring AI/BI tools like Genie can reliably discover and query business metrics.

How do I validate column references before deploying a Databricks metric view YAML?▼

You validate column references against source tables before deployment by running dedicated validation scripts included in the metric view patterns, ensuring schema accuracy and preventing query failures in Genie.

Can I implement snowflake schema joins in Databricks metric views?▼

Yes, you can implement both direct and snowflake schema joins in Databricks metric views by following the established YAML patterns, allowing complex data modeling for Genie natural language queries.

Does the Databricks metric view YAML specification support complex data modeling?▼

Yes, the metric view YAML specification supports complex data modeling by defining semantic metadata and join patterns that map business metrics to underlying tables for accurate Genie AI/BI analysis.

Why should I use YAML for semantic layer metric views instead of direct SQL?▼

Using YAML for semantic layer metric views provides standardized schema validation and metadata structure that optimizes Genie natural language processing, whereas direct SQL lacks this built-in compliance framework.

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