Presented by
Kirk Lewis
Four Ways Operators Can Fix Slowdowns and Improve Big Data Cluster Performance
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.
Pepperdata
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Real-time, automated cloud cost optimization with no manual tuning
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.