Pepperdata Capacity Optimizer autonomously and continuously reclaims waste from Spark applications in real time, empowering you to:
Reduce monthly spend and run more apps for the same cost
Drive optimal price/performance cost outcomes
Reduce inefficiencies in the cloud or on premises
Save an average of 30-47% on your Apache Spark workload costs on Amazon EMR and Amazon EKS
Minimize (or eliminate) waste in Spark to run more applications without additional spend
Free your developers from the tedium of managing individual apps so they can focus on innovation
Pepperdata saved 50% for a large software company running huge Apache Spark jobs. Let us do the same for you.
No matter where you run Apache Spark—in the cloud, on prem, or in hybrid environments—Pepperdata Capacity Optimizer saves you money by:
Automatically identifying where more jobs can be run in real time
Enabling the scheduler to more fully utilize available resources before adding new nodes or pods
Eliminating the need for manual tweaking and tuning so your team can focus on higher value tasks
The result: Apache Spark CPU, memory, and I/O resources are automatically optimized to reduce costs and increase utilization, enabling more apps to be launched at cost savings of up to 47%.
The Pepperdata Dashboard shows actual Realized Savings and Potential Savings at the cluster level so your teams know exactly how much they are saving.
You can customize your optimization levels by cluster, and view your savings in real time.