Apache Druid has always had built-in data lifecycle management by way of retention rules. Specifying fixed time intervals or relative periods, you would tell Druid to retain only data segments that are not older than x days.

In realtime analytics, a common scenario is that you want to retain a lot of (years of) historical data in order to run analytics over a longer period of time. But these analytical queries occur infrequently and their performance is usually not critical. The bulk of everyday queries, however, accesses only a limited set of relatively fresh data, typically 1 or 2 weeks worth.

With on premise setups, compute/storage separation is often implemented using a NAS or similar storage unit that exposes an S3 API endpoint.