databricks-metric-views

name	databricks-metric-views
description	Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions across teams and tools.
compatibility	Requires databricks CLI (>= v1.0.0)
metadata	{"version":"0.1.0"}
parent	databricks-core

Unity Catalog Metric Views

Define reusable, governed business metrics in YAML that separate measure definitions from dimension groupings for flexible querying.

When to Use

Use this skill when:

• Defining standardized business metrics (revenue, order counts, conversion rates)
• Building KPI layers shared across dashboards, Genie, and SQL queries
• Creating metrics with complex aggregations (ratios, distinct counts, filtered measures)
• Defining window measures (moving averages, running totals, period-over-period, YTD)
• Modeling star or snowflake schemas with joins in metric definitions
• Enabling materialization for pre-computed metric aggregations

Prerequisites

• Databricks Runtime 17.2+ (for YAML version 1.1); 17.3+ for semantic metadata (synonyms / display_name / format)
• SQL warehouse with CAN USE permissions
• SELECT on source tables, CREATE TABLE + USE SCHEMA in the target schema

Metric View Lifecycle

Task	Reference	Load when
Create	metric-view-advisor.md	Any creation task — the advisor handles the full workflow (profile schema, analyze sources, suggest, deploy). Load create-patterns.md alongside as the YAML spec and pattern reference.
YAML spec / patterns	create-patterns.md	Patterns 1–12, full YAML field reference, formatting gotchas, deployment errors, quick reference. Companion to the advisor; also load directly for pattern lookup.
Query	query-patterns.md	Writing SQL against a metric view — MEASURE() basics, filters, join rollups, window measures, Rules 1–3.
Genie integration	metric-view-advisor.md §Genie Design Rules	One-fact-source rule, base views, domain organization, naming. Agent metadata fields (comment, synonyms, display_name, format) are in create-patterns.md §YAML Field Reference.

Typical flow: advisor → create → query/validate → Genie integration (if adding to a Genie Agent).

Source-controlled deployment with Declarative Automation Bundles

To source-control a metric view, commit its complete SQL definition and execute it through a bundle-managed SQL job. DABs do not have a native metric-view resource, but a bundle-managed SQL job can apply a committed definition:

# databricks.yml
bundle:
  name: orders_metrics

variables:
  catalog: { default: main }
  schema:  { default: default }
  warehouse_id: { default: "" }

resources:
  jobs:
    deploy_orders_metrics:
      name: deploy_orders_metrics
      parameters:
        - name: catalog
          default: ${var.catalog}
        - name: schema
          default: ${var.schema}
      tasks:
        - task_key: create_metric_view
          sql_task:
            warehouse_id: ${var.warehouse_id}
            file:
              path: ../src/orders_metrics.metric_view.sql

Deploy and run:

databricks bundle deploy --target <TARGET> --profile <PROFILE>
databricks bundle run deploy_orders_metrics --target <TARGET> --profile <PROFILE>

See the official metric view bundle example.

Related Skills

• databricks-genie-agents — create, manage, and validate Genie Agents that consume the metric views built here. Metric-view design rules for Genie are in the advisor §Genie Design Rules; query rules are in query-patterns.md.
• databricks-aibi-dashboards — build AI/BI dashboards on top of metric views.
• databricks-data-discovery — explore data before creating metric views; answer questions across your workspace.
• databricks-dabs — source-control and deploy metric view SQL definitions via bundle-managed jobs.

Resources

• Metric Views Documentation
• YAML Syntax Reference
• Joins
• Window Measures (Experimental)
• Materialization
• MEASURE() Function