Sure, let’s outline a quick win plan. First, split the data into shards so each broker handles only a fraction of the load—parallelism is key. Second, use a high‑throughput queue like Kafka or Pulsar, tuned for low latency; keep the batch size tiny, maybe 10‑20 records, and use compression sparingly to avoid decode overhead. Third, push the heavy lifting to a stateless microservice layer that can scale horizontally on demand, and keep state in a fast key‑value store like Redis or Memcached so you can retrieve results in under 10ms. Finally, monitor end‑to‑end latency continuously and auto‑scale based on a pre‑defined threshold. That’s the three‑step approach I’d go for.

Thanks for the fine‑tuning. I’ll lock the partition key to a strong hash of timestamp and user ID so no hotspot. For Kafka, I’ll bump linger.ms to 5ms, still keep batches under 30 records to keep replication latency low. The CQRS layer will write to Redis via a write‑through cache so the read side stays fresh. And I’ll set an upper limit on auto‑scale, maybe a max of 10 nodes per queue, to avoid runaway costs. That should keep the pipeline razor‑sharp.

Good call—monitor that curve closely, maybe add a sliding‑window KPI so we know when the spikes are real trends. If we hit the 10‑node cap, a second tier queue will buffer the excess, and a back‑pressure signal will slow producers until the load normalizes. That way we stay under 10 ms without over‑provisioning.