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.

Gartner, Inc. estimates that worldwide spending on public cloud services is forecast to grow 20.4% to total $678.8 billion in 2024. With many organizations incorporating FinOps practices to govern how they spend their money in the cloud, Real-Time Cost Optimization is essential to saving money. In particular, as the market for Generative AI workloads continues to explode, organizations will need to consider a range of cost-savings models to extract optimal efficiency.

Apache Spark versus Kubernetes? Or both? The past few years have seen a dramatic increase in companies deploying Spark on Kubernetes (K8s). This isn’t surprising, considering the benefits that K8s brings to the table. Adopting Kubernetes can help improve resource utilization and reduce cloud expenses, a key initiative in many organizations given today’s economic climate.

With Kubernetes emerging as the de facto operating system of the cloud, capable of running almost anything, it’s not a surprise that many enterprises are rapidly porting their Apache Spark workloads to Kubernetes. This includes migrating Amazon EMR workloads to Amazon EKS to gain the additional deployment and scaling benefits of a fully managed service like Amazon EKS.

When Apache Spark works well, it works really well. Sometimes, though, users find themselves asking this frustrating question. Spark is such a popular large-scale data processing framework because it is capable of performing more computations and carrying out more stream processing than many other data processing solutions. Compared to popular conventional systems like MapReduce, Spark is 10-100x faster.

Pepperdata’s ability to halve cloud costs at top enterprises may seem radical and new, but it’s absolutely not. Pepperdata has been hardened and battle tested since 2012, and our software is currently deployed on about 100,000 instances and nodes across some of the largest and most complex cloud deployments in the world. We’re an AWS ISV Accelerate partner focused on helping customers save money running Spark on Amazon EMR and Spark and microservices on Amazon EKS.

Here at Pepperdata, we’ve been on a number of sales calls lately where there’s a sense of incredulity on the other side of the video screen. How does Pepperdata extract as much as 50 percent in cost savings from some of the most sophisticated clusters in the world, the ones that had already been optimized for peak performance by the most dedicated and talented IT teams? It almost seems too good to be true. It’s not.

In the epic struggle against spiraling cloud costs, the only durable solution is automated optimization. The manual engineering efforts that most people call “cloud cost optimization” these days can be time-consuming and tedious, if not impossible at the scale at which modern data stacks operate. Moreover, they redirect valuable human resources away from technical innovation.

You may know Pepperdata as the world’s only provider of real-time, autonomous cloud cost optimization. Pepperdata Capacity Optimizer can be installed in under an hour in most enterprise environments and goes to work immediately slashing your cloud costs with our patented resource optimization algorithms.