Explains forward and reverse engineering of data models
Generating a database from a model, and recovering a model from an existing database with the judgement recovery needs.
Can AI do this?
AI does this well Most of the tested AI models master this. 10 of 10 models master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.1-pro-preview | Flagship | 100% | Mastered |
| gpt-6-astra | Flagship | 100% | Mastered |
| claude-fable-5-1 | Flagship | 100% | Mastered |
| gemini-3.8-flash | Mid tier | 100% | Mastered |
| gpt-6.1-sol | Mid tier | 100% | Mastered |
| claude-sonnet-5-5 | Mid tier | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 100% | Mastered |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
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: Generating a database from a model, and recovering a model from an existing database with the judgement recovery needs
How it is assessed: Lists what a reverse-engineered model of a sample database cannot recover by itself
Related knobits
Needed for
- K-803 Recognises an object in the SQL missing from the model
- K-826 Recognises undetected drift between database and model
- K-851 Explains generated artefacts and their single source of truth
- K-859 Explains testing DDL by execution and detecting drift
All knobits of Data modelling.
Source: DAMA-DMBOK2 chapter 5, section 2.2.2, 3.1