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More and more warehouses ship their own semantic objects. They are the closest neighbours of canonic in one respect: they live next to the data and are meant to be queried by agents. This page covers the two large ones, then gives short notes on four smaller tools.
Last verified against the vendors’ public documentation and project pages in October 2026. These products change quickly, so check the linked sources for the current state.

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Snowflake semantic views

A semantic view is a schema-level Snowflake object. It defines logical tables and their relationships, facts, dimensions and metrics. SQL users can select from it, BI tools can use its metrics and dimensions, and Cortex Agents and the Cortex Analyst REST API read the definition to answer natural-language questions. Creating, querying and granting access to a semantic view are covered by Snowflake privileges.

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Databricks metric views

A metric view is a Unity Catalog object defined in YAML. It declares a source, dimensions, measures, filters and joins, including star and snowflake schemas. Users group by any available dimension and the engine generates the computation, instead of fixing the grouping at creation time as a standard view does. Metric views can be consumed from SQL editors, notebooks, dashboards, Genie Agents, alerts and external BI tools such as Power BI, Tableau and Sigma.

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How they compare with canonic

Read the first two rows as the real trade-off. A warehouse-native object is the shortest path if you run on one warehouse and want its own agent to use it. canonic is the better fit when you run on several, want the rules and the review history in git, or want the same answer from any agent. canonic does not read Snowflake semantic views or Databricks metric views today. Its Snowflake and Databricks connectors read the warehouse schema, not those objects. If you already maintain either, the two can sit side by side. Bind the canonical definitions in canonic, use assertions to compare their numbers with what the warehouse-native object returns, and treat any difference as a definition to resolve.

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Short notes on other tools

None of these entries is a verdict. Several are good choices if their shape fits your stack. The common thread with the tools on the other pages is the same one: a definition tells a tool how to compute a number, and canonic adds the rules, the review trail and the trust tier around it.

canonic and other tools

The roles and a side-by-side table for dbt, Cube, LookML and Ossie.

Guide: Snowflake

Running canonic on a Snowflake warehouse.

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Sources