Likely by processing different data at different times. E.g. big aggregates and time series are likely updated on a lower interval whilst near-time event data

Likely by processing different data at different times. E.g. big aggregates and time series are likely updated on a lower interval whilst near-time event data is probably stored in fast recall storage so that you can satisfy near-time queries immediately. Otherwise, their long view queries are always sampled. They control the fields available so they can make sure samples are properly stratified in order to be representative. IMO, it's just good old fashioned good engineering. It could be done with no special tech whatsoever. E.g. sharded Postgres DBs and network storage would do just fine.