Sampling vs Dask for large datasets

i need someone with more grooves in their brain to help me out here.

per the title, has anyone found it preferable to sample from a large dataset instead of training from a dask dataframe comprised of all the data?

and if so, have you found there to be a heavy tradeoff in model quality? my main gripe with dask is that the .compute() methods seem to take forever.

i am still very green to it all, so apologies if …

dask datascience datasets large datasets sampling