Open Data Infrastructure
Is a Lakehouse the Same as Open Data Infrastructure?
Lakehouse is an architecture label. Open data infrastructure is a control model. They overlap, but they are not synonyms.
A lakehouse can be open or closed. ODI is what you build when you decide you want the open version, even when it is inconvenient.
Where they overlap
Lakehouse usually describes a system that stores data in a shared storage layer and enables analytics and AI workloads to run on it. That overlaps with ODI because ODI stacks are typically built on open lakehouse primitives: open table formats, open catalogs, and multi-engine compute.
Where they differ
The difference is the commitment to control and portability.
- Lakehouse: can be a single vendor platform with open-adjacent features.
- ODI: is defined by open contracts at the boundary, so the data product remains portable across tools.
Core idea: ODI is a lakehouse with a non-negotiable portability and governance contract.
Why ODI is stricter
ODI is stricter because it treats open standards as infrastructure, not as compatibility options. It assumes you will need to change engines, catalogs, and vendors over time, and it makes that change survivable.
If your "open lakehouse" still requires one proprietary control plane to run correctly, you have not built ODI. You have built a lakehouse product.
Questions to ask
If you want to know which one you have, ask simple questions.
- Can you read and write your critical tables from at least two engines?
- Can you move the catalog and keep governance semantics intact?
- Can you export data with time travel, lineage, and policy metadata preserved?
- Does the system still work when you remove vendor-only features?
If the answer is "no," you have a lakehouse. That may still be fine. It just is not ODI.
Sources to start with
Start with open table and catalog specifications that define portable lakehouse behavior.