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.

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.

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.

In this blog series we’ll be examining the Five Myths of Apache Spark Optimization. (Stay tuned for the entire series!) If you’ve missed Myth #1, check it out here. The second myth examines another common assumption of many Spark practitioners: Cluster Autoscaling stops applications from wasting resources.

In this blog series we’ll be examining the Five Myths of Apache Spark Optimization. (Stay tuned for the entire series!) The first myth examines a common assumption of many Spark users: Observing and monitoring your Spark environment means you’ll be able to find the wasteful apps and tune them.

Cloud FinOps, Augmented FinOps, or simply FinOps, is rapidly growing in popularity as enterprises sharpen their focus on managing financial operations more effectively. FinOps empowers organizations to track, measure, and optimize their cloud spend with greater visibility and control.

Apache Spark is an open-source, distributed application framework designed to run big data workloads at a much faster rate than Hadoop and with fewer resources. Spark leverages in-memory and local disk caching, along with Apache Spark is an open-source, distributed application framework designed to run big data workloads at a much faster rate than Hadoop and with fewer resources.

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 activities to ameliorate or address this waste and the cost it incurs, things such as: … and the list goes on. These are infrastructure-level optimizations.