I've done it both ways. Look into Data Sketches also if you want to see applications. The pros: -- Samples are small and fast most of the time. -- can be use

I've done it both ways. Look into Data Sketches also if you want to see applications. The pros: -- Samples are small and fast most of the time. -- can be used opportunistically, eg in queries against the full dataset. -- can run more complex queries that can't be pre-aggregated (but not always accurately). The cons: -- requires planning about what to sample and what types of queries you're answering. Sudden requirements changes are difficult. -- data skew makes uniform sampling a bad choice. -- requires ETL pipelines to do the sampling as new data comes in. That includes re-running large backfills if data or sampling changes. -- requires explaining error to users -- Data sketches can be particularly inflexible; they're usually good at one metric but can't adapt to new ones. Queries also have to be mapped into set operations. These problems can be mitigated with proper management tools; I have built frameworks for this type of application before -- fixed dashboards with slow-changing requirements are relatively easy to handle.