Explains data federation and virtualisation
Querying distributed sources as one without moving the data.
Can AI do this?
Not settled yet The benchmark tested this, but the models' answers do not settle yet whether they master it. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 75% | Not shown |
| gemini-3.5-flash-lite | Low cost | 44% | Not shown |
| gpt-6-luna | Low cost | 75% | Not shown |
| claude-haiku-5-5 | Low cost | 75% | Not shown |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: Querying distributed sources as one without moving the data.
How it is assessed: Answers a CDMP-style multiple-choice question on the federation and virtualisation
All knobits of Data integration and interoperability.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 8: Data Integration and Interoperability, element NV-12