With interest in big data and cloud increasing around the same time, it wasn’t long until big data began being deployed in the cloud. Big data comes with some challenges when deployed in traditional, on-premises settings. There’s significant operational complexity, and, worst of all, scaling deployments to meet the continued exponential growth of data is difficult, time-consuming, and costly.

If you have ever seen the 1976 movie ‘All the President’s Men’ you may remember the phrase “follow the money.” The idea behind this is that political corruption could be exposed merely by looking at financial transfers between parties. In testing, I like to give a slight tweak on this phrase and say, ‘follow the revenue.’ What does this mean? Plainly, we should focus most of our testing efforts in the ways that we will see the most positive return.

While writing a comparison of Kubernetes and Koyeb, we tried to determine how much operating a Kubernetes cluster really costs. This section of our comparison took us hours to write and ended up being so long that we decided to write a dedicated post about it. Full disclaimer: At Koyeb, we're building a serverless platform and we have a purpose-built orchestration engine.