Design a scalable monitoring and observability system for a global microservices architecture using Prometheus and Grafana. Discuss components like data collection, aggregation, alerting, and trade-offs between push vs pull models, high cardinality metric handling, and distributed tracing integration.

Design a scalable monitoring system using Prometheus for metrics collection via pull model with service discovery, and Grafana for visualization. Use Prometheus relabeling and aggregation layers to manage high cardinality. Integrate distributed tracing with OpenTelemetry or Jaeger. Alerting via Prometheus rules and Grafana. Discuss trade-offs: pull model ensures consistency but may increase latency; push model (e.g., Pushgateway) is better for short-lived jobs. Use Thanos or Cortex for long-term storage and scalability. Balance metric retention and cardinality to avoid performance degradation.

Implement a global monitoring system using Prometheus as the primary metrics collection tool, leveraging its pull model with service discovery (e.g., Kubernetes endpoints) for consistent data collection. Deploy Grafana for centralized dashboards and alerting, integrated with Prometheus for real-time visualization. For high cardinality metrics, use relabeling rules to reduce unnecessary labels and employ Prometheus aggregation layers (e.g., sum, avg) to manage data volume. Integrate distributed tracing via OpenTelemetry, exporting traces to Jaeger or Zipkin, and visualize them in Grafana. Alerting is handled via Prometheus alerting rules (e.g., CPU thresholds) and Grafana’s alerting capabilities for complex scenarios. Trade-offs: Pull model ensures data consistency but may introduce latency for ephemeral services; Pushgateway can be used for batch jobs. For scalability, use Thanos or Cortex for horizontal scaling, long-term storage, and query federation. Employ sharding and efficient retention policies to manage costs and performance. Ensure all components are globally distributed with replication for high availability.