Big data is category of data management, processing, and storage that is primarily defined by its scale. Conventional data processing techniques and tooling are often not suitable for the volume, velocity, and variety of data generated by some modern environments, so new paradigms had to be developed. In this article, we introduce big data concepts and discuss why and how they can be useful.
Kubernetes is designed to address some of the difficulties that are inherent in managing large-scale containerized environments. However, this doesn’t mean Kubernetes can scale in all situations all on its own. There are steps you can and should take to maximize Kubernetes’ ability to scale—and there are important caveats and limitations to keep in mind when scaling Kubernetes. I’ll explain them in this article. Scale versus Performance The first thing that must be understood about scaling a Kubernetes cluster is that there is a tradeoff between scale and performance.