Monitoring applications running in Docker containers and Kubernetes requires a dynamic approach, as the system must adapt automatically to scaling changes without burdening administrators. Docker containers offer a lightweight, isolated environment for software with all necessary components, while Kubernetes facilitates the automated deployment and management of these containerized applications. Traditional monitoring definitions are expanded in this context to include collecting, parsing, storing, visualizing, and acting on logs and metrics from applications and their environments. The Elastic Stack, comprising Elasticsearch, Kibana, Beats, and Logstash, simplifies these tasks by providing modules for common log formats and leveraging container metadata for autodiscovery. This enables quick setup of visualizations and automated alerts, illustrated through a practical example in the IBM Cloud Container Service. Future discussions aim to delve deeper into the specifics of Elastic modules and configuration techniques for efficient monitoring.