Three Ways Big Data Clusters Slow Down and How Operators Can Fix These Issues - ERP Spain.com - España y América Latina

Despite tremendous progress, there remain critically important areas, including multi-tenancy, performance optimization, and workflow monitoring where the DevOps team still requires management help. In this webinar, Kirk lewis will discuss the ways that big data clusters slow down, how to fix them, and how to keep them running at an optimal level. Presented by Field Engineer Kirk Lewis, this webinar is an overview of Pepperdata operation performance management (OPM). 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. Presenter Bio Kirk Lewis joined Pepperdata in 2015. Previously, he was a Solutions Engineer at StackVelocity. Before that he was the lead technical architect for big data production platforms at American Express. Kirk has a strong background in big data.