Appreciate the Snaplet comparison, they were doing good work. You're right that realistic looking strings are the easy part. We're focused on relational integrity first (FKs, constraints, realistic cardinality), but business domain logic is the next layer. What kinds of rules would be useful for you? Things like weighted distributions, time-based patterns, conditional relationships?
The realistic cardinality is actually a good start (the problem with things like using Faker for DB seeds being that everything is entirely too random).
If one were be able to use metrics as source then, depending on the quality of the metrics, it might be possible to distribute data in a manner similar to what's observed in production? You know, some users that are far more active than others, for example. Considering a major issue with testing is that you can't accurately benchmark changes or migrations based on a staging environment that is 1% the size of your prod one, that would be a huge win I think even if the data is, for the most part, nonsensical. As long as referential integrity is intact the specifics matter less.
Domain specific stuff is harder to describe I think. For example, in my setup I'd want seeds of valid train journeys over multiple legs. There's a lot of detail in that where the shortcut is basically to try and source it from prod in some way.
This is useful. What if you ran a CLI locally that extracts just the statistical profile from prod cardinality, relationship ratios, etc. and uploaded that? We'd never touch your database, you just hand us the metrics and we match the shape.
