Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

"Why buy a cloud cost optimization solution when I can just build it myself?" Here at Pepperdata, we often hear this question. Many of our prospects and customers have gone to great lengths implementing various optimization strategies and solutions to mitigate the cost of their cloud or on-prem data centers. These homegrown solutions might include monitoring tools, manual or automated instance rightsizing initiatives, enabling autoscaling, and application tuning.

"Why buy a cloud cost optimization solution when I can just build it myself?" Here at Pepperdata, we often hear this question. Many of our prospects and customers have gone to great lengths implementing various optimization strategies and solutions to mitigate the cost of their cloud or on-prem data centers. These homegrown solutions might include monitoring tools, manual or automated instance rightsizing initiatives, enabling autoscaling, and application tuning.

If you're like most companies running large-scale, data- intensive workloads in the cloud, you’ve realized that you have significant quantities of waste in your environment. Smart organizations implement a host of FinOps and other activities to address this waste and the cost it incurs: … and the list goes on. These are infrastructure-level optimizations.

If you're like most companies running large-scale, data-intensive workloads in the cloud, you’ve realized that you have significant quantities of waste in your environment. Smart organizations implement a host of FinOps and other activities to address this waste and the cost it incurs: … and the list goes on. These are infrastructure-level optimizations.

"How can there be an extra 30% overhead in applications like Apache Spark that other optimization solutions can't touch?" That's the question that many Pepperdata prospects and customers ask us. They're surprised—if not downright mind-boggled—to discover that Pepperdata autonomous cost optimization eliminates up to 30% (or more) wasted capacity inside Spark applications.

Optimizing data-intensive workloads typically takes months of planning and significant human effort to put cost-saving tools and processes in place. Every passing day increases the risk of additional expenditures—outlays that cost the business money and time, and that cause delays to new revenue-generating GenAI or AgenticAI projects. Remove the risk from optimization with Pepperdata Capacity Optimizer’s 100% ROI Guarantee.

In this blog series we’ve examined Five Myths of Apache Spark Optimization. But one final, bonus myth remains unaddressed: Bonus Myth: I’ve done everything I can. The rest of the application waste is just the cost of running Apache Spark. Unfortunately, many companies running cloud environments have come to think of application waste as a cost of doing business, as inevitable as rent and taxes.

In this blog series we’re examining the Five Myths of Apache Spark Optimization. The fifth and final myth in this series relates to another common assumption of many Spark users: Spark Dynamic Allocation automatically prevents Spark from wasting resources.