Skill

databricks-metric-views

Name: databricks-metric-views
Author: Databricks

manage Databricks Unity Catalog metric views

Published by Databricks Updated Jul 12

Covers Data Engineering KPI Databricks Analytics

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.

SKILL.md

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

Quick Start

Inspect Source Table Schema

Before authoring a metric view, inspect the source tables. Use discover-schema as the default — one call returns columns, types, sample rows, null counts, and row count. If you only know the schema, list tables first with query "SHOW TABLES IN ...".

databricks experimental aitools tools discover-schema catalog.schema.orders catalog.schema.customers

For dimensions and measures, probe distribution beyond sampling — cardinality of candidate dimensions, min/max/percentiles for measures, top categorical values. Write aggregate SQL through databricks experimental aitools tools query --warehouse <WH> "...". Both commands auto-pick the default warehouse; set DATABRICKS_WAREHOUSE_ID or pass --warehouse <ID> to override.

The databricks experimental aitools tools subcommands are experimental — subject to change between CLI versions. Confirm availability with databricks experimental aitools tools --help before relying on them; see CLI Execution for the stable Statement Execution API fallback.

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	references/yaml-reference.md	Complete YAML spec: dimensions, measures, joins, materialization
Patterns & Examples	references/patterns.md	Common patterns: star schema, snowflake, filtered measures, window measures, ratios
Multi-source build (Advisor)	references/metric-view-advisor.md	Guided workflow to build metric views from gold schemas, dashboards, SQL queries, Genie spaces, or KPI files — analysis, overlap detection, deploy

Guided, multi-source build (Metric View Advisor)

For the single-table create/query patterns above, use this skill directly. When the user wants to build metric views from existing assets — gold/fact schemas, AI/BI dashboards, SQL query files, Genie spaces, or KPI spreadsheets — combine multiple sources, deduplicate against views that already exist, and walk deployment end to end, use the Metric View Advisor in references/metric-view-advisor.md. It builds on this skill's baseline spec and adds the multi-source analysis, overlap detection, and an interactive build/deploy flow. Load it when the user asks to "formalize our KPIs," "build a metric/semantic layer from our tables/dashboards/queries," or otherwise wants a guided build rather than authoring one view by hand.

SQL Operations

Create Metric View

CREATE OR REPLACE VIEW catalog.schema.orders_metrics
WITH METRICS
LANGUAGE YAML
AS $$
  version: 1.1
  comment: "Orders KPIs for sales analysis"
  source: catalog.schema.orders
  filter: order_date > '2020-01-01'
  dimensions:
    - name: Order Month
      expr: DATE_TRUNC('MONTH', order_date)
      comment: "Month of order"
    - name: Order Status
      expr: status
  measures:
    - name: Order Count
      expr: COUNT(1)
    - name: Total Revenue
      expr: SUM(total_price)
      comment: "Sum of total price"
$$;

Query Metric View

SELECT
  `Order Month`,
  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
LIMIT 100;

Describe Metric View

DESCRIBE TABLE EXTENDED catalog.schema.orders_metrics;

-- Or get YAML definition
SHOW CREATE TABLE catalog.schema.orders_metrics;

Grant Access

GRANT SELECT ON VIEW catalog.schema.orders_metrics TO `data-consumers`;

Drop Metric View

DROP VIEW IF EXISTS catalog.schema.orders_metrics;

CLI Execution

The databricks experimental aitools tools commands are experimental and their surface can change between CLI versions. Before relying on a subcommand (query, statement submit/get, discover-schema, get-default-warehouse), confirm it exists with databricks experimental aitools tools --help, and fall back to the stable Statement Execution API below if it isn't available. There is no stable databricks sql execute / execute-statement verb.

For short statements (SHOW/DESCRIBE/SELECT), run the SQL inline:

databricks experimental aitools tools query --warehouse WAREHOUSE_ID "SHOW TABLES IN catalog.schema"

For long DDL (CREATE OR REPLACE VIEW ... WITH METRICS LANGUAGE YAML AS $$...$$), write the SQL to a .sql file and submit the file — this avoids the $$-heredoc escaping traps (bash variable expansion, sed, JSON encoding) entirely:

# orders_metrics.sql holds the full CREATE OR REPLACE VIEW ... $$ ... $$ statement
databricks experimental aitools tools statement submit --file orders_metrics.sql --warehouse WAREHOUSE_ID
databricks experimental aitools tools statement get <statement_id>   # blocks until terminal

This is the same file-based path the Metric View Advisor uses for deployment — keep to one method so the two don't drift.

Statement Execution API (stable alternative)

If the experimental aitools commands aren't available, use the stable Statement Execution REST API, which takes the SQL as a JSON string:

databricks api post /api/2.0/sql/statements/execute --json '{
  "warehouse_id": "WAREHOUSE_ID",
  "statement": "CREATE OR REPLACE VIEW catalog.schema.orders_metrics WITH METRICS LANGUAGE YAML AS $$\nversion: 1.1\nsource: catalog.schema.orders\ndimensions:\n  - name: Order Month\n    expr: DATE_TRUNC(MONTH, order_date)\nmeasures:\n  - name: Total Revenue\n    expr: SUM(total_price)\n$$"
}'

For long statements, template the JSON from a .sql file rather than hand-escaping newlines.

Convert an Existing View to a Metric View

To migrate a regular view to a metric view, treat its SELECT source as the metric view's source, then promote GROUP BY columns to dimensions and aggregations to measures. The new metric view does not replace the original — it sits alongside it as a governed metric layer.

-- Existing regular view (keep as-is or drop later)
-- CREATE VIEW catalog.schema.orders_summary AS
-- SELECT DATE_TRUNC('MONTH', order_date) AS month,
--        SUM(total_price) AS revenue,
--        COUNT(*) AS order_count
-- FROM catalog.schema.orders
-- GROUP BY 1;

-- Equivalent metric view (new artifact, governed)
CREATE OR REPLACE VIEW catalog.schema.orders_metrics
WITH METRICS
LANGUAGE YAML
AS $$
  version: 1.1
  source: catalog.schema.orders
  dimensions:
    - name: Order Month
      expr: DATE_TRUNC('MONTH', order_date)
  measures:
    - name: Revenue
      expr: SUM(total_price)
    - name: Order Count
      expr: COUNT(1)
$$

After verifying parity (SELECT ... FROM <orders_metrics> returns the same numbers as the original view), update downstream consumers and drop the original view.

YAML Spec Quick Reference

version: 1.1                    # Required: "1.1" for DBR 17.2+ (semantic metadata needs 17.3+)
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. Semantic metadata (`synonyms`/`display_name`/`format`) needs 17.3+
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

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