Maximize the Value of Your Apache Spark Workloads
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
Secure up to 47% Cost Savings with Continuous Intelligent Tuning
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Reduce Costs
Save an average of 30-47% on your Apache Spark workload costs on Amazon EMR and Amazon EKS
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Optimize Your Apache Spark Clusters for Efficiency
Minimize (or eliminate) waste in Spark to run more applications without additional spend
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Eliminate Manual Tweaking and Tuning of Apache Spark Applications
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.
Pepperdata Optimizes Apache Spark Clusters
in the Cloud or On-Prem
No matter where you run Apache Spark—in the cloud, on prem, or in hybrid environments—Pepperdata Capacity Optimizer saves you money by:
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Automatically identifying where more jobs can be run in real time
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Enabling the scheduler to more fully utilize available resources before adding new nodes or pods
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Eliminating the need for manual tweaking and tuning so your team can focus on higher value tasks
The result: Apache Spark CPU and memory are automatically optimized to reduce costs and increase utilization, enabling more apps to be launched at cost savings of up to 47%.