Cluster Health Monitoring Dashboards for Spark and YARN Workloads

Added Jun 2025 3 design docs

Teams that run big data workloads in energy and transportation depend on Spark jobs finishing on time, yet many operate their clusters blind: a stalled executor or saturated YARN queue often goes unnoticed until a nightly pipeline fails and downstream reports arrive empty. Reliable operations demand continuous visibility into cluster health, resource usage, and job behavior across hybrid cloud environments. In this project the intern builds that observability layer. Prometheus is configured to scrape metrics from Spark applications and YARN resource managers, capturing executor memory, task throughput, queue utilization, and node health. Grafana dashboards turn those series into real-time views of cluster performance, with panels for job durations, failure rates, and capacity trends, plus alerting rules that notify operators the moment thresholds are breached. The intern also tunes scrape intervals and retention policies so the monitoring stack itself stays lightweight and trustworthy. The result is hands-on fluency in monitoring and reliability practice for data platforms: metric instrumentation, dashboard design, alert engineering, and using observability data to optimize Spark resource allocation and cluster costs.

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