Different needs dictate different design choices for optimality.
Could not agree more. Even for time series, which could be seen as a subset of OLAP, trade-offs and design choices inherent to time-series data are necessary. As an example of a TSDB that I know well, QuestDB: Data is always ordered by time once it lands on the disk, the data is partitioned by time, and the ingestion protocol is conceived to stream large volumes of data, which can be either continuous or in bursts.
Could not agree more. Even for time series, which could be seen as a subset of OLAP, trade-offs and design choices inherent to time-series data are necessary. As an example of a TSDB that I know well, QuestDB: Data is always ordered by time once it lands on the disk, the data is partitioned by time, and the ingestion protocol is conceived to stream large volumes of data, which can be either continuous or in bursts.