I think you have to be a bit careful here, since if the profiles are too different from what you'll actually see in production, you can end up regressing performance instead of improving it. E.g., imagine you use one kind of compression in test and another in production, and the FDO decides that your production compression code doesn't need optimization at all.
If you set up continuous profiling though (which you can use to get flamegraphs for production) you can use that same dataset for FDO.
Yeah, I was worried using the "wrong" profile might result in regressions. But I haven't really seen that in my tests, even when using profiles from quite different workloads (like OLTP vs. analytics, different TPC-H queries, etc.). So I guess most optimizations are fairly generic, etc.
If you set up continuous profiling though (which you can use to get flamegraphs for production) you can use that same dataset for FDO.