CRDTs work well for the specific case of machine-generated time-series because your records are samples. There are no complex relationships between records even

CRDTs work well for the specific case of machine-generated time-series because your records are samples. There are no complex relationships between records even for the same source. Gaps in the time series are like missing pixels in a picture, there is no specific pixel that is critical as long as you have enough of them to figure out what you are looking at in the moment. This also implies that there are no semantically meaningful update operations on specific records beyond converging values. Platforms like Cassandra can make some of the design choices they do because they don't support high write throughput (relatively). With sensor data models throughput and efficient use of bandwidth is everything, so minimizing the chattiness of ensuring consistency is critical.