Four Ways Operators Can Fix Slowdowns and Improve Big Data Cluster Performance

Presented by

Kirk Lewis

About this talk

Despite tremendous progress, there are critically important areas, including multi-tenancy, performance optimization, and workflow monitoring where the DevOps team still need management help. In this webinar, presenter and Pepperdata Field Engineer, Kirk Lewis discusses why big data clusters slow down, how to fix them, and how to keep them running at an optimal level. In this online webinar followed by a live Q and A, Field Engineer Kirk Lewis discusses: • How Pepperdata Cluster Analyzer helps operators overcome Hadoop and Spark performance limitations by monitoring all facets of cluster performance in real time, including CPU, RAM, disk I/O, and network usage by user, job, and task. • How Pepperdata Capacity Optimizer increases capacity utilization by 30-50% without adding new hardware • How Pepperdata adaptively and automatically tunes the cluster based on real-time resource utilization with performance improvement results that cannot be achieved through manual tuning.

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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.