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Unlock Peak Amazon EMR Workload Efficiency

with Dynamic Resource Optimization

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    Increased Resource Utilization

    Instruct the system scheduler to add resources to allocated but unused node capacity

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    Enhanced Autoscaling

    Launch new nodes only when existing node capacity is full

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    Improved Throughput Performance

    Run more containers per node

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How Autodesk Optimized its Spark Workloads on Amazon EMR by 50%

Challenge

Autodesk experienced runaway costs as the team could not keep up with manually tuning its Spark on Amazon EMR workloads.

Solution

Pepperdata Capacity Optimizer autonomously tuned Autodesk’s Spark applications in real time for maximum resource utilization.

Results

Autodesk realized cost savings by over 50% for its Spark on EMR workloads, and automated manual tuning tasks to free the developer team for more innovative, high-growth projects.

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Learn More About Pepperdata Capacity Optimizer

Case Studies

Extole Exceeds Expectations for Resource Optimization with Pepperdata to Save 30%

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Pepperdata for Amazon EMR Solution Brief
Solution Briefs

Pepperdata for Amazon EMR Solution Brief

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Pepperdata Decreases Instance Hours/Cost by 38% on Amazon EMR
Benchmarks

TPC-DS Benchmark Report: Benchmark Shows Pepperdata Decreases Instance Hours/Cost by 38% on Amazon EMR

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Looking for a safe, proven method to reduce resource waste and cost by up to 75% and maximize value for your cloud environment? Sign up now for a free Capacity Optimizer demo to see how you can start saving immediately.