Spark Performance Tuning on Kubernetes Best Practices (Part 2)

Presented by

Alex Pierce

About this talk

Running Spark on Kubernetes in a stable, performant, cost-efficient, and secure manner presents complex challenges. Spark performance tuning on Kubernetes ensures you get the best performance by optimizing system resources and tuning configurations. Join Pepperdata Field Engineer Alex Pierce as he discusses how to reduce the complexity of monitoring and managing Spark on Kubernetes with autonomous optimization and full-stack observability. Topics include: • Automation and observability for lowering costs and improving performance • Deploying, managing, monitoring, and simplifying Spark on Kubernetes: big data application monitoring, platform monitoring, and dynamic optimization • Configuring for performance and efficiency • Spark app-level dynamic allocation and cluster level autoscaling • What Spark on Kubernetes performance success looks like
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Pepperdata Capacity Optimizer delivers 30-47% greater cost savings for data-intensive workloads, eliminating the need for manual tuning by optimizing CPU and memory in real time with no application changes. Pepperdata pays for itself, immediately decreasing instance hours/waste, increasing utilization, and freeing developers from manual tuning to focus on innovation.