Monitor distributed systems by utilizing tools such as Prometheus, Grafana, or Datadog to collect and visualize metrics from various components. Set up alerts based on predefined thresholds to proactively detect and address issues before they impact the system. Implement distributed tracing with tools like Jaeger or Zipkin to track requests as they traverse different services, helping identify bottlenecks and latency issues. Utilize log aggregation tools such as ELK Stack or Splunk to centralize logs from different services and enable efficient troubleshooting.

Establish key performance indicators (KPIs) to track the health and performance of the distributed system, including metrics related to latency, error rates, throughput, and resource utilization. Monitor the system's scalability by tracking metrics such as CPU and memory usage, network traffic, and response times under varying loads. Implement automated testing and chaos engineering practices to simulate real-world scenarios and ensure the system's resilience under different failure conditions.

Regularly review and analyze monitoring data to identify trends, patterns, and anomalies that may indicate underlying issues or potential improvements. Continuously optimize monitoring configurations based on the evolving needs of the distributed system and the organization's objectives. Collaborate with cross-functional teams to ensure alignment between monitoring strategies and business goals, fostering a culture of shared responsibility for the reliability and performance of the distributed system.