We build applications very differently. SQS queues with 1000s of clients have been a go to for me for over a decade. And the opposite as well — 1000s of queues (one per client device, they’re free). Zero maintenance, zero cost when unused. Absurd scalability.
On the producer side I’d expect to see change data capture being directed to a queue fairly efficiently, but perhaps you have some intermediary that’s running between the system of record and the queue? The latter works, but yeah it eats compute.
On the consumer side the duty cycle drives design. If it’s a steady flow then a polling listener is easy to right size. If the flow is episodic (long periods of idle with unpredictable spikes of high load) one option is to put a alarm on the queue that triggers when it goes from empty to non-empty, and handle that alarm by starting the processing machinery. That avoids the cost of constantly polling during dead time.
Hey I'm curious how the consumers of those queues typically consume their data, is it some job that is polling, another piece of tech that helps scale up for bursts of queue traffic, etc. We're using the google equivalent and I'm finding that there are a lot of compute resources being used on both the publisher and subscriber sides. The use cases here I'm talking about are mostly just systems trying to stay in sync with some data where the source system is the source of record and consumers are using it for read-only purposes of some kind.