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Databricks
Unified analytics platform and lakehouse for data engineering, science, and AI
Warehouse
How nao works with Databricks
nao connects directly to Databricks and reads your full schema on every sync, so the analytics agent has an accurate view of your lakehouse tables. If you use dbt on Databricks, nao ingests your manifest automatically: model documentation, lineage, and metric definitions become part of the agent's context with no manual re-documentation.
Queries execute in your own Databricks workspace using your credentials. Business users ask questions in plain English from the nao Chat UI, Slack, or Teams, and every answer shows the generated SQL and the tables used, so data teams keep control over quality and governance.
What you can do with nao and Databricks
- Open Databricks lakehouse data to business users through plain-English questions
- Import dbt-on-Databricks documentation and lineage as agent context automatically
- Benchmark agent reliability on your Databricks question set with nao's evaluation harness
- Keep versioned context and audit logs for every query the agent runs against Databricks
