Curate, govern, and scale Databricks AI/BI Genie - Unity Catalog grounding, benchmarks, cost control, and MCP access.
What you'll learn
Stand up a curated Genie Space grounded in Unity Catalog metadata, not LLM guesswork
Diagnose why Genie chose a specific join, filter, or grain - and catch it when the SQL and the words disagree
Engineer all four curation layers: general instructions, example SQL queries, trusted assets, and metric views
Treat Unity Catalog metadata (comments, constraints, tags, certification) as a first-class product, because your schema is your prompt
Evaluate answer accuracy with benchmarks built from real ticket history instead of assuming correctness
Run a forty-five-minute weekly monitoring and feedback loop to operate Genie like a product
Embed Genie chat directly into AI/BI Dashboards and engineer the full dashboard lifecycle
Navigate the Genie One unified chat surface across Agents, dashboards, queries, metric views, and documents
Deploy and govern Genie on the mobile app for hallway-question use cases
Govern access with the four-link permission chain (CAN VIEW / CAN RUN / CAN EDIT / CAN MANAGE) and run-as semantics
Control PAYGO and DBU spend with system-table usage queries, budget policies, and chargeback tags
Expose Genie programmatically through the Conversation API's start/message/poll/result lifecycle
Give external agents grounded access to Genie through the Managed MCP Server
Author dashboards by prompt with Genie Code and migrate legacy Tableau and Power BI workbooks
Requirements
A working knowledge of SQL - SELECT, JOIN, GROUP BY, aggregations
Access to a Databricks workspace with Unity Catalog enabled (a trial or existing workspace covers every exercise - setup instructions included)
Basic familiarity with data warehouse or lakehouse concepts - not required in advance, but helpful context
No prior experience with Genie, AI/BI, or conversational analytics required - we build that from zero
Basic Python helps for the Conversation API and MCP Server modules, but is not mandatory
A laptop and a Databricks account (trial or existing) - no other tools needed
Description
Your analytics team isn't drowning in data - it's drowning in a ticket queue. 300 requests a month, an 11-day median wait, and 68 percent of them are simple filters on data you already have. Bolting an LLM straight onto the warehouse doesn't fix that - it just hallucinates joins and runs unreviewed SQL with a service account's credentials. Databricks AI/BI Genie fixes it properly, if you curate and govern it instead of treating it like a chatbot demo.
Across 25 modules and 129 lessons you follow one data team - Meridian Retail, a 40-person analytics org buried under 300 tickets a month - as they replace the queue with governed conversational analytics. You stand up the meridian.retail Unity Catalog schema yourself, ship your first working Genie Space in twenty minutes, then go deep on the part most courses skip entirely:curation. You'll engineer all four layers that separate a grounded, trustworthy agent from an expensive guessing machine - general instructions, example SQL queries, trusted assets (parameterized functions), and metric views - plus the Unity Catalog metadata hygiene (comments, constraints, tags, certification) that is, in effect, your prompt.
What makes this course different
-Curation as the core skill, not an afterthought. Four full modules on the exact levers - instructions, example queries, trusted assets, metric views - that turn a plausible-looking answer into a correct one.
-Accuracy you can prove, not assume. A dedicated benchmarks module builds evaluation sets from real ticket history, plus a forty-five-minute weekly monitoring and feedback loop to operate Genie like a product.
-Honest about maturity levels - a genuine differentiator. This course tells you plainly what's GA (Genie Code), what's Public Preview (BI workbook import, the Managed MCP Server), and what's Beta (the "Genie One" unified surface) - so you deploy with eyes open instead of finding out in production.
-Cost governance as a first-class topic. How PAYGO and DBU billing actually works, the Monday-morning system-table queries that catch runaway spend, budget policies, chargeback tags, and the save that pays for the whole rollout.
-Governance you can defend to security. The four-link permission chain (CAN VIEW / CAN RUN / CAN EDIT / CAN MANAGE), run-as semantics, Share and Individual data permissions, and the honest model-visibility map.
-Beyond the chat window. The Conversation API for programmatic access, the Managed MCP Server so external agents can ask Genie grounded questions, the mobile app for hallway questions, and Genie Code plus workbook import for migrating off legacy Tableau and Power BI dashboards.
What you'll build, module by module: the AI/BI platform map and its three persona tracks, how Genie actually answers under the hood (LLM plus Unity Catalog metadata plus SQL warehouse execution - and where the trust boundary sits), the meridian.retail demo lakehouse, your first Genie Agent in twenty minutes, the diagnosis muscle for reading why Genie chose that join or that grain, all four curation layers, Unity Catalog metadata as a product, accuracy benchmarks, the weekly monitoring loop, AI/BI Dashboards with embedded Genie chat, dashboard lifecycle engineering (authoring, publishing, the API, performance), the Genie One unified chat surface, the mobile app, the full permissions and security-boundary model, PAYGO cost governance, the Conversation API with a real Python client and retry patterns, the Managed MCP Server, Genie Code and BI migration, rollout architecture and a repeatable migration factory, the production playbook (dev-to-prod, cloning, failure modes, troubleshooting) - and the capstone that rebuilds the entire Meridian Retail platform from scratch.
The capstone - Meridian Retail End to End: you rebuild the whole platform yourself - curated Genie Space, all four curation layers, benchmarked accuracy, cost governance, and programmatic access - applying every module's skill in one build, graded against the same standards this course teaches.
By the end of this course, you will be able to stand up, curate, benchmark, govern, and expose a production Genie deployment - and know exactly which parts of the platform you can trust in production today versus which are still maturing.
Enrol now. Conversational analytics only works when it's grounded and governed - build the judgment to do that, not just the prompt to turn it on.
Who this course is for
Data engineers and analytics engineers tired of being the human bottleneck between a business question and an answer
Data platform teams evaluating whether Databricks AI/BI Genie is ready to replace ticket-driven BI requests
BI developers migrating dashboards off legacy tools like Tableau or Power BI who need an honest maturity assessment first
Anyone responsible for governing PAYGO/DBU cost or Unity Catalog access on a Databricks workspace
Analytics leads who want to operate conversational analytics like a product - benchmarked, monitored, and cost-controlled
Engineers who want to expose grounded analytics to programs and agents via the Conversation API or MCP Server



https://rapidgator.net/file/cde9d97aab3e50b9a7f76839e41abc58/Databricks_Genie_Masterclass_Governed_AI_BI_Analytics.part1.rar.html
https://rapidgator.net/file/18aa98b9a894c81448bb49bcffc311f4/Databricks_Genie_Masterclass_Governed_AI_BI_Analytics.part2.rar.html
https://rapidgator.net/file/180388826cf22132860cb5112a6d9eeb/Databricks_Genie_Masterclass_Governed_AI_BI_Analytics.part3.rar.html