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Pepperdata

Pepperdata

You Can Solve the Overprovisioning Problem

Feb 3, 2025 By Pepperdata In Pepperdata
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.
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Pepperdata

You Can Solve the Overprovisioning Problem

Feb 3, 2025 By Pepperdata In Pepperdata
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.
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Pepperdata

Pepperdata "Sounds Too Good to Be True"

Dec 10, 2024 By Pepperdata In Pepperdata
"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.
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Pepperdata

100% ROI Guarantee: You Don't Pay If You Don't Save

Nov 6, 2024 By Pepperdata In Pepperdata
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.
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Pepperdata

Bonus Myth of Apache Spark Optimization

Aug 29, 2024 By Pepperdata In Pepperdata
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.
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Pepperdata

Myth #5 of Apache Spark Optimization: Spark Dynamic Allocation

Aug 14, 2024 By Pepperdata In Pepperdata
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.
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Myth #5 of Apache Spark Optimization | Spark Dynamic Allocation

Aug 14, 2024 By Pepperdata In Pepperdata
Spark Dynamic Allocation is a useful feature that was developed through the Spark community’s focus on continuous innovation and improvement. While Apache Spark users may believe Spark Dynamic Allocation is helping them eliminate resource waste, it doesn’t eliminate waste within applications themselves. Watch this video to understand SDA's benefits, where it falls short, and the solution gaps that remain with this component of Apache Spark.
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Pepperdata

Myth #4 of Apache Spark Optimization: Manual Tuning

Jul 25, 2024 By Pepperdata In Pepperdata
In this blog series we’ve been examining the Five Myths of Apache Spark Optimization. The fourth myth we’re considering relates to a common misunderstanding held by many Spark practitioners: Spark application tuning can eliminate all of the waste in my applications. Let’s dive into it.
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Myth #4 of Apache Spark Optimization | Manual Tuning

Jul 19, 2024 By Pepperdata In Pepperdata
Manual tuning can remediate some waste, but it doesn’t scale or address in-application waste. Watch this conversation to learn why manually tuning your Apache Spark applications is not the best approach to achieving optimization with price and performance in mind. Visit Pepperdata's page for information on real time, autonomous optimization for Apache Spark applications on Amazon EMR and EKS.
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Pepperdata

Myth #3 of Apache Spark Optimization: Instance Rightsizing

Jun 26, 2024 By Pepperdata In Pepperdata
In this blog series we are examining the Five Myths of Apache Spark 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 Spark users: Choosing the right instances will eliminate waste in a cluster.
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