How do you manage monitoring data to prevent data overload?

Monitoring data refers to metrics and logs collected from systems, applications, and services, such as CPU usage or error logs, used for real-time insight into performance health. Its importance lies in ensuring system reliability, early fault detection, and application in cloud-native environments like Kubernetes clusters or containerized platforms to prevent downtime. The management core includes data sampling, aggregation, and selective storage: configuring filtering rules, downsampling rates, and compressing old data through tools like Prometheus to avoid redundant metrics; in practice, setting data retention policies (e.g., retaining only 7 days of historical data), using labels to filter relevant metrics, and implementing alert threshold automation to reduce noise affecting analysis efficiency. Implementation steps are: 1. Define key business metrics such as latency or error rate; 2. Apply sampling mechanisms (e.g., Prometheus' scrape_interval); 3. Configure storage optimizations like tiered storage or data lifecycle management; 4. Integrate toolchains such as Elasticsearch and Grafana for automated analysis. Business value includes reducing storage costs by 20%-50%, improving monitoring accuracy, and accelerating problem response.