Open Data Infrastructure
Polaris vs Nessie vs Gravitino: Open Catalog Options
These tools live in the same conversation, but they solve different problems. Choose based on the contract you need to standardize.
If your catalog decision is really an interoperability decision, you need to stop evaluating catalogs like they are UIs.
The real question you are answering
Open data infrastructure depends on stable interfaces between engines, storage, metadata, and governance. Catalog conversations get noisy because people use the word "catalog" to mean three different things: a REST catalog protocol, a transactional metadata store with branching semantics, and a federated metadata lake that sits above many systems.
Apache Polaris, Project Nessie, and Apache Gravitino show up together because they are all trying to make metadata more interoperable. They are not interchangeable.
Core idea: pick the catalog based on which contract you need to standardize: cross-engine access, cross-table transactions, or cross-system metadata governance.
What each project is trying to be
A plain framing that keeps you honest:
- Apache Polaris: an open source catalog for Iceberg that exposes an interoperable interface (including the Iceberg REST protocol) and a governance model around it. See the project docs and repository for the scope and current behavior. Docs, GitHub.
- Project Nessie: a transactional catalog for data lakes with Git-like semantics. It focuses on versioning, branching, and multi-table commits at the catalog layer. Project site, Spec.
- Apache Gravitino: a federated metadata lake that aims to unify metadata across data sources and engines, rather than being only an Iceberg catalog. Project site.
If you want to separate the layers before comparing products, read Table Format vs Catalog vs Query Engine first.
When you need a REST catalog
If your problem is "multiple engines need to share the same Iceberg tables," a standard catalog protocol is the hard requirement. The Iceberg REST Catalog specification exists because per-engine catalog plugins did not scale, especially across languages and vendors. Iceberg REST Catalog specification.
That problem is less about UI features and more about interface stability: one endpoint, one client, and predictable credential vending. If the catalog is the choke point, the rest of your open lakehouse is theater.
When you need branching and multi-table commits
Some teams need more than "a place to find tables." They need catalog-level versioning so they can make coordinated changes across many tables without leaving the system in a half-applied state. That is the heart of the Nessie pitch: Git-like semantics for catalog state. Nessie specification.
This is most compelling when you have:
- multi-table pipelines where partial writes are operationally dangerous
- release workflows that need branches, promotion, and rollback
- teams that treat data changes like software changes
This is also where you should be careful. Git metaphors are attractive. Operational semantics still have to match your engines, your storage, and your failure model.
When you need metadata federation
Sometimes the real pain is not Iceberg catalogs. It is that you have too many metadata systems. Your warehouse has one model. Your lake has another. Your streaming platform has another. Your AI stack has its own tags, vectors, and prompts. Your governance story becomes a spreadsheet that tries to reconcile them all.
That is the problem space Gravitino is aiming at: federated metadata and governance across diverse sources and engines. If your "catalog" needs to cover more than Iceberg tables, you are in this category. Apache Gravitino.
A decision model that holds up
Use this decision model:
- If the problem is cross-engine Iceberg access: prioritize a REST-compatible catalog interface, clear auth and policy, and predictable operational behavior.
- If the problem is coordinated multi-table change management: prioritize catalog versioning semantics (branching, promotion, rollback), and test the operational edge cases aggressively.
- If the problem is fragmented metadata across the entire stack: prioritize federation and governance integration, not just table registration.
ODI pushes you to define which contract you are buying. If you try to buy all three at once, you will overfit to a tool instead of designing the boundary you actually need.
Sources to start with
Start with the project documentation and the relevant specifications.