How can business context work across multiple clouds and data platforms?

Summary

• Business context works across clouds when the definitions live in a governance layer that spans them, not inside a single BI tool or warehouse. In Databricks, Unity Catalog governs data, metrics, and AI assets across AWS, Azure, and GCP under one permission model.
• A semantic layer defines KPIs once as governed objects, so a metric like "active customer" means the same thing in every dashboard, notebook, and AI agent.
• Genie Ontology (in Public Preview) learns context from the dashboards, queries, metric views, and notebooks you already have, ranks definitions by authority, and injects only permission-aware context into answers.
• Lakehouse Federation lets teams query data where it already lives, including external warehouses, operational databases, and cloud object storage, all under the same Unity Catalog policies.
• Definitions stay portable because they are stored in open formats and reachable over SQL, APIs, and the Model Context Protocol (MCP), so any tool can use them without lock-in.

How can business context work across multiple clouds and data platforms?

Business context travels across clouds and platforms when your definitions live in a governance and semantic layer that spans them, rather than being trapped inside one BI tool or one warehouse. The practical requirement is a single place to define what business terms mean, apply one permission model, and expose those definitions to every tool that asks. On Databricks, Unity Catalog is that layer. It governs tables, files, models, metrics, and AI agents across AWS, Azure, and GCP, so a definition and its access rules stay consistent no matter where the data or the user sits.

What is a business context layer?

A business context layer captures the meaning behind your data: how key metrics are calculated, which sources are authoritative, and what rules apply. It sits between raw tables and the people or agents asking questions. Without it, every team re-derives "revenue" or "active user" in its own SQL, and answers drift apart. With it, those definitions are declared once as governed objects and reused everywhere. Unity Catalog semantics supply the building blocks: metric views that define reusable KPIs, domains that group assets into business-aligned areas, and certification signals that mark trusted sources.

Key capabilities to evaluate

When you judge whether a context layer can actually work across clouds and platforms, these criteria matter most.

Capability	Why it matters
Cross-cloud governance	One permission, lineage, and audit model across AWS, Azure, and GCP so rules do not fork per cloud.
Define-once semantics	Metrics and terms modeled as governed objects, reused by BI, notebooks, and agents without redefinition.
Federated access	Query data where it lives (external warehouses, operational databases, object storage) without copying it first.
Permission-aware context	The layer surfaces only the definitions and data a given user is allowed to see.
Open, portable formats	Definitions kept in open formats and reachable via SQL, APIs, and open standards like MCP, which avoids lock-in.
Conflict resolution	A clear, authoritative answer when two teams define the same metric differently.

How Databricks approaches cross-platform business context

Databricks centers this on Unity Catalog and, for natural-language questions, Genie Ontology. Unity Catalog applies the same governance, lineage, and row- and column-level security across the three major clouds, and its semantic objects (metric views, domains, and business glossary terms) let you certify definitions that both people and AI reuse. Lakehouse Federation extends that reach so teams can query trusted data where it resides, across external warehouses, operational databases, and cloud object storage, all under Unity Catalog policies.
Genie Ontology, currently in Public Preview, adds an automatic context layer on top. It learns from assets you already have, including dashboards, SQL queries, metric views, and notebooks, and continuously builds a map of business meaning. It ranks competing definitions by authority using an algorithm called OntoRank, prefers certified sources when definitions conflict, and gates every snippet behind Unity Catalog permissions, so an answer draws only on context the requester is allowed to see. The Unity Catalog business glossary and the ontology reinforce each other: certified concepts enrich the ontology, and useful inferred concepts can be promoted back into the glossary.
Portability is the last piece. Databricks is open-sourcing metric view semantics into Apache Spark and Unity Catalog, and those definitions are reachable over SQL, REST APIs, and the Model Context Protocol. A business definition stays usable by first-party surfaces and outside tools alike, instead of being locked to one vendor.

Use cases

• A global company runs analytics on different clouds by region but wants one definition of "gross margin." Modeling it as a governed metric view means every regional dashboard and every AI agent reports the same number.
• A team keeps transactional data in an operational database and historical data in the lakehouse. Federated queries join both under one policy, without a copy step.
• An analytics group migrating between platforms keeps its glossary and metrics intact, because the semantics live in the governance layer rather than in a departing tool.

FAQs

Does business context stay consistent across AWS, Azure, and GCP?

Yes. Unity Catalog governs data, metrics, and AI assets under one model across all three clouds, so definitions and access rules do not fork per cloud.

Do I have to move data into one platform for context to work?

No. Lakehouse Federation lets you query data where it already lives, including external warehouses and operational databases, while Unity Catalog applies consistent policies.

Is Genie Ontology generally available?

Genie Ontology is in Public Preview. Treat its behavior and availability as subject to change, and confirm current status in the Databricks documentation.

How does the context layer handle conflicting definitions?

Genie Ontology ranks definitions by authority with OntoRank and prefers certified business definitions, so the most trusted meaning wins when teams disagree.