Let me clarify this by breaking down my approach. If I notice latency and data loss during peak loads, my first step is to check the monitoring dashboards and logs to identify where the bottleneck or failure is happening. I would look at API response times, error rates, and throughput metrics, focusing on whether the delays are happening at the API gateway, in the backend services, or at the database level.If the latency is at the API layer, I would review the load balancer and API gateway configurations to ensure they are distributing requests evenly. I would also check for any resource exhaustion, such as CPU or memory spikes, on the servers running the APIs.If the issue seems related to the messaging queue, I would examine queue lengths and processing times to see if messages are backing up. In that case, scaling out the consumers or optimizing the message processing logic might help.For data loss, I would investigate if any requests are being dropped or if there are failed writes to the database. This could involve checking retry policies, dead-letter queues, and database transaction logs. If the database is the bottleneck, I would look at query performance and consider sharding or adding replicas to handle the increased load.To put it another way, my troubleshooting involves isolating the problem area, analyzing logs and metrics, and then applying targeted solutions like scaling resources, optimizing code, or improving error handling and retries. I would also run load tests in a staging environment to validate the fixes before deploying them to production.