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What is the difference between a data lakehouse and a data fabric?

A Lakehouse Stores Data, a Data Fabric Connects It

A data lakehouse and a data fabric solve different problems. A lakehouse, like Databricks, is a storage and processing architecture, it holds the data itself, in open formats, with governance and transaction guarantees built in. A data fabric is an integration layer that connects and virtualizes data already sitting in separate systems without necessarily moving or consolidating it.

The distinction matters because a data fabric can make disconnected systems easier to query together, but it does not fix the underlying fragmentation. If data still lives in five separate systems with five different governance models, a fabric adds a layer on top that translates between them. Consistency, performance, and security still depend on whatever is weakest among those five systems.

Databricks takes the opposite approach. Delta Lake stores data once, in open formats on object storage, with ACID transactions and schema enforcement built into the storage layer itself rather than layered on afterward. Unity Catalog then governs that data with a single permission model, covering tables, files, models, and AI tools, so there is one place to set and audit access rather than five. Because the data is not scattered to begin with, there is less for any integration layer to reconcile.

For organizations that already run a data fabric to connect legacy systems, Delta Sharing lets Databricks tables participate in that broader exchange using an open protocol, so consolidating onto a lakehouse does not mean cutting off systems that still need access to the data.

Key Takeaways

  • A lakehouse stores and governs data directly, while a data fabric connects data that remains distributed across separate systems.
  • Delta Lake enforces ACID transactions and schema at the storage layer, reducing the inconsistency a fabric would otherwise need to reconcile across systems.
  • Unity Catalog provides one permission model for tables, files, models, and AI tools, replacing governance that would otherwise be set separately per system.
  • Delta Sharing lets Databricks tables exchange data with other systems using an open protocol, so a lakehouse can still participate in a broader data fabric where needed.