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 is the Big Data performance company. Fortune 1000 enterprises depend on Pepperdata to manage and optimize the performance of Hadoop and Spark applications and infrastructure. Developers and IT Operations use Pepperdata solutions to diagnose and solve performance problems in production, increase infrastructure efficiencies, and maintain critical SLAs. Pepperdata automatically correlates performance issues between applications and operations, accelerates time to production, and increases infrastructure ROI. Pepperdata works with customer Big Data systems on-premises and in the cloud.