Amazon Timestream uses quorum writes as an example. https://aws.amazon.com/blogs/aws/store-and-access-time-series-data-at-any-scale-with-amazon-timestream-now-

Amazon Timestream uses quorum writes as an example. https://aws.amazon.com/blogs/aws/store-and-access-time-series-data-at-any-scale-with-amazon-timestream-now-generally-available/ https://aws.amazon.com/blogs/aws/store-and-access-time-serie... "This is made possible by the way Timestream is managing data: recent data is kept in memory and historical data is moved to cost-optimized storage based on a retention policy you define. All data is always automatically replicated across multiple availability zones (AZ) in the same AWS region. New data is written to the memory store, where data is replicated across three AZs before returning success of the operation. Data replication is quorum based such that the loss of nodes, or an entire AZ, does not disrupt durability or availability. In addition, data in the memory store is continuously backed up to Amazon Simple Storage Service (S3) as an extra precaution." Feels like apples to oranges comparison, as the consistency models are really different. Then again, I woul definitely optimize for performance on most timeseries use cases. Different products with different features baked in.