Data Platform Observability

An observability layer purpose-built for streaming data workloads — collecting Flink, Kafka, and Kubernetes metrics and turning them into dashboards and alerts that surface problems before data goes stale.

Generic infrastructure monitoring doesn't answer the questions that matter for a streaming pipeline: is consumer lag growing, are checkpoints succeeding, is a job silently failing to make progress. The goal was a monitoring setup tailored to those pipeline-specific failure modes.

• Must monitor both application-level metrics (job health, consumer lag) and infrastructure-level metrics (CPU, memory, pod restarts).
• Alerting must be routed by severity so non-critical warnings don't page anyone at 3 a.m.
• Metrics collection must scale with the number of jobs without manual dashboard duplication.

• Defined the metrics-collection pipeline using Grafana Alloy and Prometheus-compatible remote write into Mimir.
• Built Grafana dashboards for job health, resource usage, and pipeline throughput.
• Configured alert rules and routing for checkpoint failures, consumer lag, and resource exhaustion.

Grafana Alloy agents scrape metrics from Flink job managers, Kafka exporters, and Kubernetes nodes/pods, then remote-write them into Mimir for long-term, horizontally scalable storage. Grafana queries Mimir to render dashboards, and alert rules evaluate against the same metric streams to trigger notifications when thresholds are crossed.

• Standardized on Prometheus-compatible metric formats so the same dashboards work across Flink, Kafka exporter, and Kubernetes metrics.
• Used Grafana Alloy as the collection agent to centralize scrape configuration instead of managing per-service Prometheus instances.
• Wrote alert rules directly against key pipeline signals: consumer lag growth rate, checkpoint failure count, and job restart frequency.

• Use Mimir for long-term metrics storage instead of standalone Prometheus.

  Mimir's horizontally scalable, long-term storage avoids the retention and scaling limits of a single Prometheus instance as the number of monitored jobs grows.

• Route alerts by severity rather than sending everything to one channel.

  Separating paging-worthy alerts (checkpoint failures, job crash loops) from informational warnings keeps on-call response focused on what actually needs immediate attention.

• Distinguishing real consumer-lag problems from expected, temporary spikes.

  Alerted on sustained lag growth over a rolling window rather than an instantaneous threshold, reducing noisy alerts during normal traffic bursts.

A dashboard and alerting layer that gives on-call engineers a single place to see job health, resource pressure, and pipeline throughput, with alert routing tuned to reduce noise.

• Verified alert rules against synthetic failure scenarios (manually failing a checkpoint, throttling a consumer).
• Reviewed dashboards with the team supporting the pipelines to confirm the surfaced metrics matched real triage needs.

• This project is the observability layer itself — dashboards for job health, resource usage, throughput, and consumer lag.
• Alert routing configured by severity to the appropriate notification channel.

• Read-only, scoped credentials for metrics scraping targets.
• Dashboard access restricted to the engineering team operating the pipelines.

Verified results have not been published for this project yet.

• Alerting on trends (rate of change) rather than static thresholds significantly reduced false-positive pages.
• Dashboards designed with the on-call engineer's triage flow in mind get used far more than generic metric dumps.

• Add automated anomaly detection for metrics that don't have an obvious static threshold.
• Expand dashboards to cover data-quality signals alongside infrastructure health.

• Grafana
• Prometheus
• Grafana Alloy
• Mimir
• Kubernetes
• Apache Flink
• Apache Kafka