Maximize the Value of Your Apache Spark Workloads

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.

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Pepperdata Optimizes Apache Spark Clusters
in the Cloud or On-Prem

New instances are only launched when existing ones are fully utilized.

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%.

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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 Cost Optimization Proof-of-Value to see how Pepperdata Capacity Optimizer can help you start saving immediately.