ParthGulavani7

Databricks Genie Ontology — AI skill for Claude Code

AI community

A working Databricks Genie Ontology setup (metric views, domains, pages, certification) on a retail star schema — tested live against Genie One and a Genie Agent, with research on Power BI and AI-agen.

How to install Databricks Genie Ontology

This entry records only its repository, not the path inside it, so there is no exact command to give. Open ParthGulavani7/databricks-genie-ontology and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Databricks Genie Ontology does

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README

Genie Ontology on Databricks: Setup, Research & Findings

A working Databricks **Genie Ontology** setup (Unity Catalog semantics: metric views, domains, pages, certification) built on a small "Northwind Retail" star schema, plus research into how it compares and connects to Power BI and external AI agents like Claude.

What's in here

`docs/` — the write-ups: what Genie Ontology is, how this setup was built step by step, live tests against the Genie Agent and Genie One, and four research findings comparing it to Power BI and to AI-agent access patterns.
`setup/` — the actual notebooks used to build the setup in Databricks (catalog/schema/tables, metric views, Genie Agent, domains, certification, pages). Credit: adapted from the public demo repo databeli/genie_ontology, run against our own workspace (catalog genie_ontology_demo, schema retail_demo).
`screenshots/` — screenshots of the live setup: the schema, a metric view and its measure definitions, business domains, a business definition page, the Genie Agent's configuration and test run, and a live Genie One query.
`final-docs/` — the polished deliverables: a slide deck summarizing the setup and research findings for a team walkthrough (genie-ontology-deck.pptx), and a captioned Word doc of the setup screenshots (genie-ontology-setup-screenshots-v4.docx).

Quick summary

Genie Ontology is Unity Catalog's governed semantic layer for Databricks Genie: it gives Genie (and any AI agent reading from it) the business context raw tables don't carry on their own; definitions, formulas, groupings, and trust signals, so answers are accurate and consistent instead of guessed. This project builds that layer for a small retail dataset (4 dimension tables, 3 fact tables, 3 metric views, 4 business domains, 3 definition pages, one Genie Agent), tests it live against both th