While autoscaling provides the elasticity that customers require for their big data workloads, it can lead to exorbitant runaway waste and cost and management complexity. Estimating the right number of cluster nodes for a workload is difficult; user-initiated cluster scaling requires manual intervention, and mistakes are often costly and disruptive. Join Pepperdata Field Engineer Alex Pierce for this discussion about operational challenges associated with maintaining optimal big data performance in the cloud, what milestones to set, and recommendations on how to create a successful cloud migration framework.


Learn More About How to Drive Cloud Performance on Amazon EMR with Autoscaling

If you enjoyed the webinar and would like to learn more about how Pepperdata can help you improve cloud performance with Amazon EMR autoscaling, try one of the following resources:

Curve Pattern

Take the Pepperdata Interactive Demo to See What Big Data Success Looks Like

Sign up to see how automatic optimization and full-stack observability can improve performance across your entire big data stack. No data or installation is required. Simply log in to start playing in the Pepperdata sandbox.

Explore More

Looking for a safe, proven method to reduce waste and cost by up to 47% and maximize value for your cloud environment? Sign up now for a free waste assessment to see how Pepperdata Capacity Optimizer Next Gen can help you start saving immediately.