Databricks Genie Explained: AI/BI Natural Language Analytics

What is Databricks Genie

Genie is Databricks' answer to a problem every data team has: dashboards cover the questions someone thought to build in advance, but the actual question a stakeholder asks in a meeting is almost always a slightly different cut of the same data. Genie is built to answer that follow-up question directly, in a chat interface, by generating and running SQL on the fly rather than requiring a new dashboard tile or a Slack message to the data team.

It sits under the broader AI/BI umbrella alongside AI/BI Dashboards, and it's built on the same foundation: Unity Catalog for governance and lineage, and the Databricks SQL engine to actually execute the generated query. Nothing about a Genie conversation bypasses your existing table permissions — if a user's role can't see a table today, Genie can't surface it in an answer either.

How Genie turns questions into SQL

When someone types "what was revenue by region last month" into a Genie space, three things happen before a query ever runs:

1. Genie identifies the relevant tables from the set the space is scoped to, using table and column descriptions in Unity Catalog plus any written instructions.
2. It checks for a matching or similar trusted example query already curated for that space — a close match dramatically improves accuracy over generating SQL from scratch.
3. It generates SQL, prioritizing certified metric views (governed, pre-defined business metrics) over ad hoc aggregation when one exists for the requested measure, then executes it and returns a table or chart.

Multi-turn conversation is supported, so a follow-up like "now break that down by product line" reuses context from the previous answer instead of forcing the user to restate the full question — closer to how an analyst would actually work through a request.

Genie spaces and trusted assets

A Genie space (Databricks also calls these Genie agents) is the unit you actually configure and share. It's scoped to a domain — a "Sales" space limited to revenue and pipeline tables, a "Support" space limited to ticket data — rather than exposing every table in the workspace to every question.

What determines whether that space gives good answers is almost entirely the trusted assets attached to it:

• Example SQL queries — real, correct queries a data engineer writes and accepts into the space's context, which Genie pattern-matches new questions against.
• Instructions — plain-language notes clarifying business terms ("'active customer' means an order in the last 90 days") that raw column names can't convey on their own.
• Certified metric views — governed metric definitions built on Unity Catalog, so "revenue" resolves to one agreed-upon calculation instead of Genie re-deriving it differently each time.

Databricks' own guidance is explicit that these aren't optional polish — a space with no example queries and no instructions is meaningfully worse at answering correctly than one with even a handful of curated examples.

Agent Mode: multi-step reasoning

Some questions can't be answered by one SQL query, however well-written — "why did churn spike in March" needs several angles compared against each other before a real answer emerges. Agent Mode handles that case: instead of one text-to-SQL pass, it plans and runs multiple steps, then returns a report with citations, supporting tables, and visualizations pulled together from that multi-step exploration.

Recent updates have extended what a Genie space can pull into that reasoning process — including connecting external sources like Gmail, SharePoint, and Google Drive, so an Agent Mode answer can incorporate context that never lived in a Databricks table in the first place, alongside memory of a user's stated preferences across sessions.

Setting up a Genie space that works

1. Scope it narrowly. Pick one business domain and its supporting tables rather than exposing the entire lakehouse — narrower context means fewer wrong-table guesses.
2. Add certified metric views first for any number stakeholders will ask about repeatedly (revenue, active users, churn) so Genie has one governed definition to reach for.
3. Seed 10-20 example SQL queries covering the real questions your team actually gets asked — pull these from query history rather than inventing them, since real usage patterns are what Genie needs to pattern-match against.
4. Write instructions for ambiguous business terms before anyone else uses the space — "quarter" starting on a fiscal boundary, or "customer" excluding internal test accounts, are exactly the kind of thing a schema alone can't convey.
5. Test with questions you already know the answer to before sharing the space widely, and keep refining example queries based on where it gets things wrong.

Genie vs. writing the SQL yourself

Genie is built for the person asking the question, not the person maintaining the space. If you already write SQL, going straight to a query is often still faster and more precise than phrasing a question for Genie and checking its output — especially for anything with an edge case you already know to filter for. Where Genie earns its place is the stakeholder who doesn't write SQL at all and would otherwise be blocked waiting on a data team's queue, or the analyst exploring an unfamiliar dataset who wants a fast first pass before writing anything by hand.

The two aren't in competition, in other words — the SQL skill just moves from "answer every ad hoc question personally" to "build the trusted assets that let Genie answer them correctly instead."

Common mistakes

• Launching a space with zero trusted assets. An unconfigured Genie space is guessing at column meaning from names alone — expect low accuracy until examples and instructions are added.
• Scoping a space too broadly. Including every table "just in case" gives Genie more opportunities to pick the wrong one for an ambiguous question.
• Letting metric definitions live in two places. If a metric view says one thing and an example query computes it differently, Genie's answers become inconsistent depending on which it matches against.
• Treating every answer as final. Spot-check Genie's generated SQL the way you'd review a colleague's query, especially before a number gets repeated in a meeting or report.
• Never revisiting the space after launch. Trusted assets need the same maintenance as any documentation — stale examples referencing renamed or dropped columns quietly degrade accuracy over time.