Big data organizations increasingly rely on Kubernetes to automatically manage scaling and application deployments within their containerized environments. Due to its speed and flexibility, Spark is the #1 big data application running on Kubernetes, according to a recent survey of enterprise users. In just a few lines of code, data scientists and engineers can use Spark to parallelize large amounts of work across a big data cluster. However, as big data applications move from Spark on legacy systems to Spark on Kubernetes, Spark application performance often suffers.
While Kubernetes can reduce operating costs and make deployment more agile, it also increases the management complexity of a dynamic and diverse combination of virtual machines, containers, and applications. If teams don’t have comprehensive visibility and automation built into their big data infrastructure, reliability and performance issues can be difficult to predict and diagnose. This can often result in additional, significant operational cost.
Pepperdata big data solutions provide comprehensive visibility into Kubernetes health and performance in real time. Managers, developers, and operations teams can monitor cluster resource usage and optimize the performance of their clusters through a self-service portal. Through this portal they can also manually tune Spark applications while autonomously optimizing resources at run time.
Traditional infrastructure monitoring and manual tuning methods present significant scaling and speed limitations. Pepperdata automatically optimizes Kubernetes resources while providing a correlated and granular understanding of the applications and infrastructure. For Spark on Kubernetes, Pepperdata provides:
Learn how to reduce the complexity of monitoring and managing Kubernetes with automated full-stack observability, spot Kubernetes performance management success, and more.
The Pepperdata dashboard enables your teams to visualize Kubernetes data and get actionable insights and alerting. Because Pepperdata solutions are designed to scale, teams can easily keep tabs on their Kubernetes environments—whether they’re running tens or thousands of nodes.
Pepperdata machine learning across clusters, containers, pods, nodes, users, and workflows gives you a complete understanding of your environment. This combination of manual and autonomous tuning delivers the best price/performance for Spark apps.
Additionally, full-stack observability provides you with actionable information to debug complex Spark applications, and autonomous optimization ensures that the compute resources are used efficiently. In addition to knowing that there is an issue, understand why, and quickly resolve it.
Although many monitoring vendors claim to have full observability capabilities, they typically only offer a portion of the picture and not complete observability. Pepperdata big data performance solutions provide you with the observability you need to optimize the performance of your big data deployment and improve collaboration across your teams.
Sign up to see how automatic optimization and full-stack observability can improve performance across your entire big data stack. No data or installation is required. Simply log in to start playing in the Pepperdata sandbox.