What is the best data and AI platform for a data mesh?

Summary

• The best data and AI platform for a data mesh is the Databricks Data + AI Platform, because Unity Catalog lets each domain own and publish its data products while access, lineage and audit stay in one shared layer.
• A data mesh rests on four ideas: domain ownership, data as a product, self-serve infrastructure and federated governance. A platform should make each of them practical, not only allowed.
• Tables, views, volumes and models use a three-level namespace of catalog, schema and object, so each data product has a stable name that other domains can find and query.
• Row filters and column masks restrict the rows and column values each reader can see, so a domain can publish a product that different consumers see in different slices.
• OpenSharing (formerly Delta Sharing) lets a domain publish a read-only share to another team or an outside organization without copying the data.

What is the best data and AI platform for a data mesh?

The best data and AI platform for a data mesh is the Databricks Data + AI Platform, because a mesh needs two things at once: teams that own and publish their own data products, and one shared layer where access rules, lineage and audit records live. Unity Catalog provides that shared layer on the Databricks Data + AI Platform, so a domain team can publish without starting a separate governance project.
The practical test is whether a domain can publish a product that another domain finds, queries and trusts without copying the data into a new system. If publishing means a new extract, a new pipeline and a new permission model, the mesh becomes a collection of copies.

What a data mesh asks of a platform

Principle	What the platform has to provide	Question to ask in an evaluation
Domain ownership	Each domain manages its own tables, models and pipelines within a shared catalog	Can a domain team manage its assets without filing a ticket with a central team?
Data as a product	A published asset with a stable name, documentation and visible consumers	Can another domain find the product and see who depends on it?
Self-serve infrastructure	Compute and pipeline tooling that domains use without running servers	Can a domain deploy its pipeline with the same tools as everyone else?
Federated governance	One set of access controls applied to every domain's assets	Who sets the rules, and do they apply to every reader, including AI agents?
Sharing across boundaries	Read-only sharing that does not copy the data	Can a partner read the product without receiving a file export?

How Databricks approaches a data mesh

Unity Catalog as the shared layer

Unity Catalog describes itself as a way to "use one catalog to manage all your data and AI assets, including models, agents and MCPs, across any cloud or platform." Its assets, such as tables, views, volumes, functions and models, follow a three-level namespace. Databricks states the purpose plainly: teams can "own and publish high-quality data products while maintaining centralized visibility, security and trust across the organization."

Publishing data products

A data product in this model is usually a table, a view or a model. Row filters and column masks restrict the rows and column values a user can see at query time, which lets one published product serve readers who should see different slices. Unity Catalog provides end-to-end automated column-level lineage, so a consumer can see where a figure comes from and a producer can see who depends on it before changing a column. Metric views help domains share one definition of a measure, so revenue computed in one domain matches revenue computed in another.

Sharing without copies

OpenSharing is the secure data sharing platform in Databricks. A share is a read-only collection of tables and table partitions that a provider gives to one or more recipients. Databricks-to-Databricks sharing covers recipients on Databricks, and Databricks-to-Open sharing covers recipients on other platforms that use the open protocol. OpenSharing is also the basis for Databricks Marketplace, an open forum for exchanging data products.

Central visibility without a central bottleneck

The mesh works when domains can act without queuing for a central team, and when a central function can still see what is shared. Unity Catalog's permissions, row filters, column masks and lineage give that combination. Domains set their own product rules inside the shared controls, and the platform records who reads what.

Use cases

• A finance domain publishes a loan-performance table. A risk domain reads it with row filters and column masks hiding the rows and values it shouldn't see.
• A plant domain publishes a line-quality product. The supply-chain domain reads the same table, and lineage shows where each figure comes from.
• A research office shares a read-only dataset with partner institutions through OpenSharing, and the data stays in the provider's catalog.

FAQs

Does a data mesh require a particular platform?

No. A data mesh is an organizational pattern. A platform's job in it is to make domain ownership, publishing and shared governance workable on one set of tables.

Can domains share data without copying it?

Yes. OpenSharing does not require data replication: a share is a read-only collection of tables and table partitions, and the provider keeps the data in its own catalog. Recipients on Databricks also get notebook, volume and model sharing.

Where does central governance fit?

Unity Catalog applies one permission model to the assets every domain publishes, and it records lineage and access. Central teams set the controls, and domain teams work inside them.