Stop Manually Tuning and Start Getting ROI From Your Big Data Infrastructure

Presented by

Pepperdata Field Engineer, Eric Lotter

About this talk

Would your big data organization benefit from automatic capacity optimization that eliminates manual tuning and enables you to run 30-50% more jobs on your Hadoop clusters? As analytics platforms grow in scale and complexity on-prem and in the cloud, managing and maintaining efficiency is a critical challenge, and money is wasted. In this webinar, Pepperdata Field Engineer Eric Lotter discusses how your organization can: – Maximize your infrastructure investment – Achieve up to 50 percent increase in throughput and run more jobs on existing infrastructure – Ensure cluster stability and efficiency – Avoid overspending on unnecessary hardware – Spend less time in backlog queues On a typical cluster, hundreds and even thousands of decisions are made per second, increasing typical enterprise cluster throughput up to 50 percent. Even the most experienced operator dedicated to resource management can’t make manual configuration changes with the required precision and speed. Learn how to automatically tune and optimize your cluster resources, and recapture wasted capacity. Eric will provide relevant use case examples and the results achieved to show you how to get more out of your infrastructure investment.

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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.