Why CloudWatch Leaves Data Engineering Teams Blind to the Spark Failures That Matter Most on EKS

CloudWatch, AWS's default observability tool, provides robust infrastructure monitoring for EKS clusters but falls short in capturing critical application-layer signals necessary for understanding Spark job failures, such as executor heartbeat health, shuffle degradation, and job-level failure context. While CloudWatch effectively monitors node and container metrics, it lacks native capabilities to interpret Spark-specific telemetry, making it challenging for data engineering teams to diagnose issues without custom pipelines for exporting and correlating Spark metrics. Prometheus offers a partial solution by exposing Spark metrics through its system, yet it requires additional instrumentation and management. To bridge these gaps, a unified observability model that integrates Spark application telemetry with Kubernetes lifecycle events and infrastructure utilization is recommended, reducing the operational burden and enhancing incident response. Solutions like the xLake data plane health monitor aim to provide this comprehensive observability for Spark-on-Kubernetes environments, addressing the limitations of using CloudWatch alone for Spark on EKS deployments.

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