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Pepperdata

Pepperdata Helps Karpenter Work Better

Jul 1, 2025 By Pepperdata In Pepperdata
Running Kubernetes on AWS? You're probably using Karpenter, the open-source autoscaler that dynamically provisions new instances as your EKS workloads grow. Karpenter launches rightsized instances in real time in response to pending pods, based on available instance types and the resources applications need. It also terminates underutilized nodes to reduce costs.
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Pepperdata

Myth #5 of Kubernetes Resource Optimization: Spark Dynamic Allocation

Jun 27, 2025 By Pepperdata In Pepperdata
In this blog series we’re examining the Five Myths of Kubernetes Resource Optimization. The fifth and final myth in this series relates to another common assumption of many Kubernetes users: Dynamic Allocation for Apache Spark applications automatically prevents Spark from overprovisioning resources while improving workload utilization levels.
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Pepperdata Resource Optimization for Data Workloads on Kubernetes

Jun 26, 2025 By Pepperdata In Pepperdata
Struggling with underutilized Kubernetes resources or rising cloud costs? Learn how Pepperdata Capacity Optimizer delivers real-time, automated resource optimization for Kubernetes and Amazon EMR workloads—helping teams reduce costs and boost performance without manual tuning. In this video, discover how Pepperdata helps DevOps, platform engineers, and FinOps teams.
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Pepperdata

Myth #4 of Kubernetes Resource Optimization: Manual Tuning

Jun 20, 2025 By Pepperdata In Pepperdata
In this blog series we’ve been examining the Five Myths of Kubernetes Resource Optimization. The fourth myth we’re considering relates to a common misunderstanding held by many Kubernetes practitioners: manual application tuning can increase resource utilization in my applications. Let’s dive into it.
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Pepperdata

Myth #3 of Kubernetes Resource Optimization: Instance Rightsizing

Jun 12, 2025 By Pepperdata In Pepperdata
In this blog series we are examining the Five Myths of Kubernetes Resource Optimization. So far we’ve looked at Myth 1: Observability and Monitoring and Myth 2: Cluster Autoscaling. Stay tuned for the entire series! The third myth addresses another common assumption of many Kubernetes practitioners: Choosing the right instances will eliminate waste in a cluster.
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Real-Time, Automated Resource Optimization for Kubernetes Workloads

May 16, 2025 By Pepperdata In Pepperdata
Struggling with underutilized Kubernetes resources or rising cloud costs? Learn how Pepperdata Capacity Optimizer delivers real-time, automated resource optimization for Kubernetes and Amazon EMR workloads—helping teams reduce costs and boost performance without manual tuning. In this video, discover how Pepperdata helps DevOps, platform engineers, and FinOps teams.
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Pepperdata In Collaboration with AWS | Optimize Utilization and Cost for Kubernetes Workloads

May 15, 2025 By Pepperdata In Pepperdata
In this AWS Startup Partner Spotlight, discover how Pepperdata empowers cloud-native startups to optimize their Kubernetes and Amazon EMR workloads in real time. With automated resource optimization, companies can reduce costs by an average of 30% while increasing utilization by up to 80%—without any manual tuning. Whether you're scaling rapidly or managing unpredictable workloads, Pepperdata ensures your infrastructure runs efficiently and cost-effectively from day one.
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Pepperdata

Why Manual Tuning Fails: A Better Way to Optimize Kubernetes Workloads

May 15, 2025 By Pepperdata In Pepperdata
As a data platform engineer, you’re tasked with running complex workloads—Apache Spark jobs, AI/ML pipelines, batch ETL—across dynamic Kubernetes environments. Performance matters. Time spent tuning matters. And so does cost. But if you’re still relying on manual resource tuning to optimize your workloads, you’re playing a losing game. Sure, you can tweak CPU and memory requests by hand. You can comb through Prometheus metrics, look at job logs, estimate peaks.
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Pepperdata

Increase Resource Utilization up to 80%-Automatically

Apr 9, 2025 By Pepperdata In Pepperdata
Companies running Kubernetes workloads often discover significant and unexpected waste or underutilized resources in their compute environment. Smart organizations implement a host of FinOps activities to mitigate this waste and the cost it incurs: … and the list goes on. But these are infrastructure-level optimizations that don’t address waste within an application.
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