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.

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)
• SQL warehouse with CAN USE permissions
• SELECT on source tables, CREATE TABLE + USE SCHEMA in the target schema

Quick Start

Inspect Source Table Schema

Before creating a metric view, call get_table_stats_and_schema to understand available columns for dimensions and measures:

get_table_stats_and_schema(
    catalog="catalog",
    schema="schema",
    table_names=["orders"],
    table_stat_level="SIMPLE"  # Use "DETAILED" for cardinality, min/max, histograms
)

Create a Metric View

CREATE OR REPLACE VIEW catalog.schema.orders_metrics
WITH METRICS
LANGUAGE YAML
AS $$
  version: 1.1
  source: catalog.schema.orders
  comment: "Orders KPIs for sales analysis"
  filter: order_date > '2020-01-01'
  dimensions:
    - name: Order Month
      expr: DATE_TRUNC('MONTH', order_date)
      comment: "Month of order"
    - name: Order Status
      expr: CASE
        WHEN status = 'O' THEN 'Open'
        WHEN status = 'P' THEN 'Processing'
        WHEN status = 'F' THEN 'Fulfilled'
        END
      comment: "Human-readable order status"
  measures:
    - name: Order Count
      expr: COUNT(1)
    - name: Total Revenue
      expr: SUM(total_price)
      comment: "Sum of total price"
    - name: Revenue per Customer
      expr: SUM(total_price) / COUNT(DISTINCT customer_id)
      comment: "Average revenue per unique customer"
$$

Query a Metric View

All measures must use the MEASURE() function. SELECT * is NOT supported.

SELECT
  `Order Month`,
  `Order Status`,
  MEASURE(`Total Revenue`) AS total_revenue,
  MEASURE(`Order Count`) AS order_count
FROM catalog.schema.orders_metrics
WHERE extract(year FROM `Order Month`) = 2024
GROUP BY ALL
ORDER BY ALL

Reference Files

Topic	File	Description
YAML Syntax	yaml-reference.md	Complete YAML spec: dimensions, measures, joins, materialization
Patterns & Examples	patterns.md	Common patterns: star schema, snowflake, filtered measures, window measures, ratios

MCP Tools

Use the manage_metric_views tool for all metric view operations:

Action	Description
create	Create a metric view with dimensions and measures
alter	Update a metric view's YAML definition
describe	Get the full definition and metadata
query	Query measures grouped by dimensions
drop	Drop a metric view
grant	Grant SELECT privileges to users/groups

Create via MCP

manage_metric_views(
    action="create",
    full_name="catalog.schema.orders_metrics",
    source="catalog.schema.orders",
    or_replace=True,
    comment="Orders KPIs for sales analysis",
    filter_expr="order_date > '2020-01-01'",
    dimensions=[
        {"name": "Order Month", "expr": "DATE_TRUNC('MONTH', order_date)", "comment": "Month of order"},
        {"name": "Order Status", "expr": "status"},
    ],
    measures=[
        {"name": "Order Count", "expr": "COUNT(1)"},
        {"name": "Total Revenue", "expr": "SUM(total_price)", "comment": "Sum of total price"},
    ],
)

Query via MCP

manage_metric_views(
    action="query",
    full_name="catalog.schema.orders_metrics",
    query_measures=["Total Revenue", "Order Count"],
    query_dimensions=["Order Month"],
    where="extract(year FROM `Order Month`) = 2024",
    order_by="ALL",
    limit=100,
)

Describe via MCP

manage_metric_views(
    action="describe",
    full_name="catalog.schema.orders_metrics",
)

Grant Access

manage_metric_views(
    action="grant",
    full_name="catalog.schema.orders_metrics",
    principal="data-consumers",
    privileges=["SELECT"],
)

YAML Spec Quick Reference

version: 1.1                    # Required: "1.1" for DBR 17.2+
source: catalog.schema.table    # Required: source table/view
comment: "Description"          # Optional: metric view description
filter: column > value          # Optional: global WHERE filter

dimensions:                     # Required: at least one
  - name: Display Name          # Backtick-quoted in queries
    expr: sql_expression        # Column ref or SQL transformation
    comment: "Description"      # Optional (v1.1+)

measures:                       # Required: at least one
  - name: Display Name          # Queried via MEASURE(`name`)
    expr: AGG_FUNC(column)      # Must be an aggregate expression
    comment: "Description"      # Optional (v1.1+)

joins:                          # Optional: star/snowflake schema
  - name: dim_table
    source: catalog.schema.dim_table
    on: source.fk = dim_table.pk

materialization:                # Optional (experimental)
  schedule: every 6 hours
  mode: relaxed

Key Concepts

Dimensions vs Measures

	Dimensions	Measures
Purpose	Categorize and group data	Aggregate numeric values
Examples	Region, Date, Status	SUM(revenue), COUNT(orders)
In queries	Used in SELECT and GROUP BY	Wrapped in MEASURE()
SQL expressions	Any SQL expression	Must use aggregate functions

Why Metric Views vs Standard Views?

Feature	Standard Views	Metric Views
Aggregation locked at creation	Yes	No - flexible at query time
Safe re-aggregation of ratios	No	Yes
Star/snowflake schema joins	Manual	Declarative in YAML
Materialization	Separate MV needed	Built-in
AI/BI Genie integration	Limited	Native

Common Issues

Issue	Solution
SELECT * not supported	Must explicitly list dimensions and use MEASURE() for measures
"Cannot resolve column"	Dimension/measure names with spaces need backtick quoting
JOIN at query time fails	Joins must be in the YAML definition, not in the SELECT query
MEASURE() required	All measure references must be wrapped: MEASURE(\name`)`
DBR version error	Requires Runtime 17.2+ for YAML v1.1, or 16.4+ for v0.1
Materialization not working	Requires serverless compute enabled; currently experimental

Integrations

Metric views work natively with:

• AI/BI Dashboards - Use as datasets for visualizations
• AI/BI Genie - Natural language querying of metrics
• Alerts - Set threshold-based alerts on measures
• SQL Editor - Direct SQL querying with MEASURE()
• Catalog Explorer UI - Visual creation and browsing

Resources

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