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Codd AI vs Databricks Metric Views
Databricks Unity Catalog metric views define measures, dimensions and joins once in Unity Catalog, so SQL, AI/BI dashboards and Genie agents use consistent metrics on data in Databricks. Codd AI is an AI-native contextual semantic layer that discovers business knowledge, generates the model and metrics for people to certify, and works across platforms and tools. If your analytics live entirely in Databricks, metric views are a natural starting point; Codd AI adds the wider business context and runs on top of your Databricks data alongside other platforms.
Last reviewed October 2026, based on public documentation. Spotted something out of date? Let us know.
At a glance
| Capability | Codd AI | Databricks Metric Views |
|---|---|---|
| What it is | AI-native contextual semantic layer across your data platforms | Metric definitions registered in Unity Catalog (part of Unity Catalog Business Semantics) |
| How the model is built | AI discovers entities, relationships and metric logic from data, BI assets, documents and experts; people certify | YAML definitions created with SQL DDL or Catalog Explorer; Genie Code can suggest measures and import Power BI or Tableau files |
| Business knowledge beyond the database | Built in: definitions, rules and knowledge from documents, BI reports and experts | Display names, formats and synonyms on metrics; Genie agents add instructions |
| Data scope | Snowflake, Databricks, BigQuery, Redshift, PostgreSQL, Oracle, Teradata, Trino | Data governed in Databricks Unity Catalog |
| AI and natural language | Conversational analytics built in, plus MCP, Slack and Teams; works with any LLM | Genie agents and Genie One built on metric views |
| BI tools | Power BI, Tableau, Looker, Excel | AI/BI dashboards, SQL with MEASURE(), and partner BI tools such as Sigma |
| Governance | Human certification, lineage and access control | Unity Catalog permissions, lineage, row filters and column masks |
| Repeatable analysis and automation | Playbooks and Actions | Through Databricks jobs, alerts and Genie |
What is Databricks Metric Views?
Unity Catalog metric views are the core of Databricks' Unity Catalog Business Semantics, announced as generally available in April 2026. A metric view defines measures separately from dimensions on top of a source table, view or query, including multi-level joins.
Metric views are defined in YAML and created with SQL DDL or the Catalog Explorer UI. Genie Code can suggest measures, dimensions and synonyms, and can import Power BI and Tableau files into metric views.
They are queried with SQL using the MEASURE() function, power AI/BI dashboards and Genie agents, and are available to partner BI tools. Because they live in Unity Catalog, they inherit its permissions, lineage, row filters and column masks.
Key differences
Defining metrics vs discovering business context
Metric views give Databricks teams a governed place to define metrics. Codd AI starts earlier, discovering the business knowledge itself across data, BI assets, documents and experts, then generating the ontology, data model and metrics for people to certify.
One platform vs many
Metric views describe data governed in Unity Catalog and serve Databricks' AI and BI tools. Codd AI keeps one certified context across Databricks and other platforms, and serves it to Power BI, Tableau, Looker, Excel, Slack, Teams, MCP clients and any LLM.
From answers to action
Codd AI adds Playbooks that make AI reasoning repeatable and Actions that turn insights into scheduled, human-approved execution, on the same certified context. See what is a semantic layer.
Which should you choose?
Choose Databricks Metric Views if
- Your analytics data lives entirely in Databricks and your team is comfortable defining metrics there.
- You mainly want AI/BI dashboards and Genie agents on a well-understood set of tables.
Choose Codd AI if
- You want AI to discover and generate the semantic layer, with people certifying it.
- Your data spans Databricks and other platforms, or your users work in several BI tools.
- Business knowledge lives in documents, BI reports and experts, not only in tables.
- You want Playbooks and Actions on top of trusted answers.
Using Codd AI with Databricks
Databricks is one of Codd AI's supported data platforms. Teams keep their data and governance in Databricks while Codd AI builds and certifies the business context on top, and serves it to every BI tool, agent and LLM, including data that lives outside Databricks.
Frequently asked questions
What are Databricks metric views?
Metric views are Unity Catalog objects that define measures, dimensions and joins once, so SQL queries, AI/BI dashboards and Genie agents calculate metrics consistently on data in Databricks.
Does Codd AI work with Databricks?
Yes. Databricks is a supported data platform for Codd AI, so you can build Codd AI's certified business context directly on your Databricks data.
Do I need Codd AI if I use Databricks metric views?
Metric views define metrics inside Databricks. Codd AI helps you discover and certify those definitions from your wider business knowledge, keeps them consistent across platforms and tools, and adds conversational analytics, Playbooks and Actions.
See Codd AI on your own data
Learn how Codd AI builds a certified, contextual semantic layer in weeks, and how it fits alongside the tools you already use.