Monitoring Kubernetes with Machine Learning

Presented by

Alex Pierce

About this talk

Monitoring Kubernetes is not enough to ensure your implementation will be a success. Managing Kubernetes is a combination of resource allocation, autoscaling, and configuring cloud instances. But for all the advantages Kubernetes offers large organizations, it also introduces complexity. As Kubernetes projects grow, the amount of configuration required also grows. Maintaining and updating a Kubernetes environment often becomes a task all on its own. This is in addition to making sure that the applications running on Kubernetes are operating to meet their SLAs. Join us for a webinar with Pepperdata Field Engineer Alex Pierce on controlling the complexity of your Kubernetes production platform by understanding its challenges and following best practices. Topics include - Building a robust Kubernetes-based production platform: requirements and considerations - Determining the right time to scale pilot projects into production deployments - Simplifying end-to-end Kubernetes management - Optimizing Spark performance on Kubernetes, Spark-app level dynamic allocation, and cluster level autoscaling - Configuration tips for performance and efficient resource sharing - Kubernetes monitoring and security best practices

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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 soluions 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.