Operations | Monitoring | ITSM | DevOps | Cloud

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.

Pepperdata’s cost slashing capabilities are legendary: for the last decade, we’ve been doing just that for some of the largest and most demanding enterprises in the world, including security-conscious global banks, members of the Fortune Five, and other top-tier companies.

No matter how complicated your monthly cloud bill, all spend in the cloud is premised on a very simple formula.

IT spend on big data systems in 2023 is forecasted to be $222 billion (Statista.com: Information spending on data center systems worldwide from 2012 to 2023). Actual monetary cost is just one parameter: infrastructure resources, personnel, and the overall time taken to retrieve big data insights push spending even further.

Cloud FinOps is rapidly becoming popular among organizations in today’s digital age due to its ability to help manage financial operations more efficiently. This is because it allows organizations to track, measure, and optimize their cloud spend with greater visibility. It also helps improve operational efficiency by automating numerous financial processes, including billing, budgeting, auditing, and reporting.

Kubernetes is fast becoming the world’s most preferred open-source container orchestration tool. Used for building and managing cloud-native microservices-based apps as well as the containerization of existing apps, its adoption is continuously increasing. Already, 61% of enterprises have adopted Kubernetes, and 30% are planning to embrace it within the next 12 months.