A Comparative Study of Monitoring Telemetry Pipelines with Prometheus and Grafana in Cloud-Native Java Applications

Glomb Asgarova Caspary

A Comparative Study of Monitoring Telemetry Pipelines with Prometheus and Grafana in Cloud-Native Java Applications

Authors

Glomb Asgarova Caspary

Cloud-Native Observability and Monitoring Engineer, Japan

Keywords:

Telemetry Pipeline, Prometheus, Grafana', Cloud-Native Java, Microservices Monitoring, JMX Exporter, Metrics Collection, Observability

Synopsis

In cloud-native Java applications, monitoring telemetry pipelines is critical for observability, performance tuning, and reliability. This paper presents a comparative study of two popular open-source tools—Prometheus (for metrics collection and alerting) and Grafana (for visualization and dashboarding)—in the context of a microservices-based Java application deployed on Kubernetes. We instrument the Java services using JMX exporters, configure Prometheus to scrape metrics, and build Grafana dashboards to visualize latency, throughput, error rates and JVM metrics. Key evaluation criteria include setup complexity, scalability (scrape frequency and time-series retention), query performance, alerting responsiveness, dashboard usability, and resource overhead. Our results show that while Prometheus excels at time-series collection and alerting, Grafana significantly enhances interpretability via dashboards, but careful design is needed to scale effectively. We also identify trade-offs around data retention, cardinality, and query workload. Practical recommendations for cloud-native Java developers are provided.

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