1 link tagged with all of: semantic layer + sql + metrics sql
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Rill’s Metrics SQL lets you define business metrics once and query them using plain SQL across dashboards, notebooks, and AI agents. It compiles metric views into optimized OLAP queries, handling grouping, filters, time functions, and security automatically.
- Define a metric once in a YAML-backed metrics view and query it as plain SQL from dashboards, notebooks, and AI agents alike, instead of redefining it in dbt, Looker, Python, etc.
- A parser/compiler/executor pipeline rewrites simple queries like "SELECT country, revenue FROM revenue_metrics" into full OLAP SQL with correct GROUP BY, joins, and filters automatically.
- Security (row-level and column permissions) and SQL-injection-safe parameter binding are enforced consistently at the executor stage for every consumer.
- Works across multiple backends (ClickHouse, Snowflake, DuckDB, Druid) and can leverage existing database optimizations like materialized views and indexes for performance.