How can AI understand data across different systems?

Summary

• The key is a shared context and governance layer that spans your systems, not a separate AI bolted onto each tool. On Databricks, Unity Catalog governs managed and external data under one model, so AI interprets terms the same way everywhere.
• You can query data where it lives. Lakehouse Federation connects to external databases, warehouses, and catalogs and lets AI answer questions against them without first copying the data into one place.
• Definitions travel with the data. A metric view defined once in Unity Catalog resolves to the same governed calculation whether the question comes from a dashboard, a SQL query, or a natural-language agent.
• Genie Ontology unifies modeled and inferred context. It combines the semantics you curate with knowledge it extracts automatically from existing assets, giving AI a business-aware map across sources. It is in Public Preview.
• Permissions and lineage stay consistent across sources, so reasoning over many systems never bypasses access controls.

How can AI understand data across different systems?

For AI to understand data spread across different systems, it needs one layer that describes what the data means and governs who can see it, wherever that data physically sits. On Databricks, that layer is Unity Catalog. It governs managed tables, files, and external sources under a single model, and its semantics give AI consistent definitions across the estate. Lakehouse Federation extends the same governance to data in external systems so AI can answer against them in place, and Genie Ontology, in Public Preview, sits on top to give AI a business-aware map that spans those sources.

Why data across different systems is hard for AI

Enterprise data rarely lives in one place. Customer records sit in one database, events in another, finance in a warehouse, and reference data in files or an external catalog. Each system names things its own way, enforces its own permissions, and models the same concept differently. Point an agent at that sprawl without a unifying layer and it guesses at joins, confuses one definition for another, and can cross permission boundaries it shouldn't. Understanding data across systems means resolving those differences into one consistent, governed view.

What to look for in a cross-system context layer

The evaluation is less about any single connector and more about whether meaning and governance hold together across sources.

Capability	Why it matters
Unified governance across sources	One permission and lineage model so AI respects access rules regardless of where data lives.
Query data in place	Reach external systems without copying everything into a central store first.
Consistent metric definitions	A term like 'active customer' resolves to one governed calculation across every source and surface.
Automatic context extraction	Learns relationships and definitions from existing assets, so you don't hand-model every system.
Permission-aware answers	AI uses only the data and context a given user is allowed to see, and cites its sources.
Open, standards-based access	Context is reachable by the tools and agents your teams already use, not locked to one interface.

How Databricks approaches it

Unity Catalog is the governance and semantic foundation. It governs structured tables, unstructured files, models, and metrics under one model, with lineage from source to dashboard. Its semantics layer lets you define metric views, domains, and Pages once, so every consumer resolves the same meaning. Lakehouse Federation connects to external databases, warehouses, and catalogs and brings them under that same governance, so a natural-language agent can answer a question against a federated source without a pipeline to move the data first. On top, Genie Ontology combines the context you model in Unity Catalog with context it infers automatically from existing dashboards, queries, notebooks, and Genie Agents, then ranks it by authority and gates it by Unity Catalog permissions. The effect is AI that reasons across your whole estate from one trusted, governed foundation. For a walkthrough of answering questions over data wherever it lives, see Talk to all your data, wherever it lives. Because Genie Ontology is in Public Preview, treat its behavior as an emerging capability and confirm details in the documentation.

Use cases

• A business user asks a cross-system question in plain language, and the agent resolves it to a certified metric and generates correct SQL against the right sources.
• A data team exposes data held in an external warehouse to AI without standing up a second copy or a new pipeline.
• Several agents and BI tools share the same governed definitions, so an answer means the same thing no matter which tool produced it.

FAQs

Does AI have to copy all my data into one place first?

No. Lakehouse Federation lets Databricks query many external sources in place and bring them under Unity Catalog governance, so AI can answer against them without a migration step.

How do definitions stay consistent across systems?

A metric view defined in Unity Catalog resolves to the same governed calculation everywhere, so a term means the same thing in a dashboard, a SQL query, or a natural-language answer.

What keeps cross-system answers secure?

Unity Catalog applies one permission and lineage model across managed and federated sources, and Genie Ontology gates every context snippet by those permissions, so AI uses only what a user is allowed to see.

Is Genie Ontology generally available?

No. Genie Ontology is in Public Preview. Confirm current capabilities in the Databricks documentation before relying on them.