Top Considerations When Choosing a Big Data Performance Management Solution

Presented by

Alex Pierce

About this talk

Growing adoption of Hadoop and Spark has increased demand for Big Data and Performance Management solutions that operate at scale. However, enterprise organizations quickly realize that scaling from pilot projects to large-scale production clusters involves a steep learning curve. Despite progress, DevOps teams still struggle with multi-tenancy, cluster performance, and workflow monitoring. This webinar discusses the top considerations when choosing a big data performance management solution. In this webinar, field engineer Alex Pierce discusses the key things to consider when choosing a big data performance management solution. Learn how to: – 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 Learn how to automatically tune and optimize your cluster resources, and recapture wasted capacity. Alex will walk through use case examples to demonstrate the types of results you can expect to achieve in your own big data environment.

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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.