It is not necessarily the case that data warehouses have high update latency. Open source streaming tech (e.g. Apache Kafka, Beam, etcetera) can be used to build an OLAP database updated in near real-time.

Sure, I've done this many times myself. The big caveat is that this only works if your data/update velocity is relatively low. I've seen many operational data models where the data warehouse would never be able to keep up with the operational data flow. Due to the rapidly increasing data intensity of business, there is a clear trend where this latter situation will eventually become the norm. I've already seen instances at multiple companies where Kafka-based data warehouse pipelines are being replaced with monolith architectures because the velocity of the operational system is too high.

For it to work, online OLAP write throughput needs to scale like operational databases. This is not the case in practice, so operational databases can scale to a point where the OLAP system can't keep up. The technical solution is to scale the operational system to absorb the extra workload created by the data warehousing applications, but current database architectures are not really designed for it so it isn't trivial to do.

Our data is near real time. But I can see how latency can be an issue based on size of the data and transformations are needed on the data before it hits the DW. But there are solutions - kafka being one.