How to Optimize Data Management in Containers with Kubernetes and DataStax

The use of containers has significantly accelerated modern application development due to their efficiency compared to virtual machines. They allow applications and dependencies to be packaged together into a minimal deployable image, simplifying the "works on my machine" problem. Containers also enable developers to move applications between environments while ensuring they behave as expected. DevOps teams can improve delivery speed and ship better software at scale. However, managing container assignment to servers and handling load balancers and network rules led to the creation of container orchestration platforms like Kubernetes. This platform handles assigning containers to servers, connecting them together, and monitoring their health. It also restarts containers if they go down and schedules replacements on other hardware. Stateful workloads have become more viable with components that keep stateful workloads in mind, such as software-defined storage backends where data can follow the container wherever it is scheduled or a scheduler that knows it cannot simply spin things back up. DataStax Enterprise (DSE) simplifies development by going masterless and making all nodes equal, capable of handling read and write requests without a single point of failure. Docker images for DSE, DSE OpsCenter, and DataStax Studio have been released for production use, significantly reducing testing time from hours to minutes. The DataStax Enterprise Operator for Kubernetes manages the lifecycle of individual Kubernetes resources, simplifying the process of managing the distributed data platform, DSE.