Running and Optimizing Analytics Workloads on Amazon EKS

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Hands-on workshop Level 400

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About the event

In this advanced 400-level workshop, you'll leverage Amazon EKS Auto Mode and Karpenter to autonomously optimize cost-performance for Apache Spark workloads — eliminating undifferentiated infrastructure management. Using MCP-driven observability, you'll visualize job-level costs in real time to inform migration, expansion, and optimization decisions.

What You'll Learn:

  • Deploy Spark jobs on EKS Auto Mode with zero manual node management
  • Compare cost-performance across Spot instances, NVMe storage, and Graviton — with autonomous compute orchestration
  • Optimize analytics workloads using intelligent placement powered by Karpenter
  • Instrument MCP-based cost observability for per-job spend analysis
  • Make data-driven cost-performance decisions for production analytics at scale
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