Kubernetes: What Is It and Why Use It?

What is Kubernetes?

Kubernetes, often abbreviated K8s, is an open-source container orchestration platform originally designed by Google, then donated to the Cloud Native Computing Foundation (CNCF) in 2014. The project draws inspiration from Borg, Google's internal system that manages billions of containers every week. Kubernetes automates the deployment, scaling, load balancing, and lifecycle management of containerised applications.

Unlike Docker Compose, which orchestrates containers on a single machine for development, Kubernetes is designed to manage clusters of machines in production. It distributes containers across multiple nodes (servers), monitors their health, automatically restarts them upon failure, and adjusts the number of replicas based on load. It is essentially an operating system for the cloud.

At Kern-IT, we are well-versed in Kubernetes and recommend it to clients whose needs justify this level of complexity. For the majority of Belgian SMEs, a classic deployment with Docker, Nginx, and Gunicorn on a Linux server remains the most pragmatic and cost-effective solution. But when a project requires high availability, automatic scaling, or multi-region deployment, Kubernetes becomes the reference tool.

Why Kubernetes Matters

Kubernetes has profoundly transformed how companies deploy and manage their applications. Its massive industry adoption is built on concrete, measurable benefits.

• High availability: Kubernetes distributes containers across multiple nodes and automatically restarts those that fail. Users experience no service interruption, even in the event of hardware failure.
• Automatic scaling: the Horizontal Pod Autoscaler (HPA) automatically adjusts the number of service replicas based on metrics like CPU usage or request count. During a traffic spike, Kubernetes adds instances; when load decreases, it removes them to reduce costs.
• Zero-downtime deployments: rolling updates allow you to update an application progressively, pod by pod, without downtime. If something goes wrong, automatic rollback restores the previous version within seconds.
• Multi-cloud portability: Kubernetes runs identically on AWS (EKS), Azure (AKS), Google Cloud (GKE), or bare-metal. This portability prevents vendor lock-in and enables migration between cloud providers.
• Rich ecosystem: Helm for packaging, Istio for service mesh, Prometheus for monitoring, ArgoCD for GitOps — the Kubernetes ecosystem covers every aspect of infrastructure management.

How It Works

Kubernetes relies on a master-worker architecture. The control plane comprises the API Server (entry point for all operations), etcd (key-value database storing cluster state), the Scheduler (which decides on which node to place each pod), and the Controller Manager (which ensures the cluster's actual state matches the desired state).

Worker nodes execute the containers. Each node hosts a Kubelet (agent communicating with the control plane), a kube-proxy (network management), and a container runtime (Docker, containerd, or CRI-O). The basic deployment unit is the Pod, which encapsulates one or more containers sharing the same network and storage.

Deployments are described declaratively via YAML files. You define the desired state — number of replicas, Docker image, allocated resources, health check rules — and Kubernetes takes care of reaching and maintaining it. Services expose pods to the network, Ingress manages external HTTP routing, and ConfigMaps and Secrets store configuration securely.

Concrete Example

Consider a Belgian e-commerce platform that experiences traffic spikes during sales and end-of-year holidays. The Django application is containerised with Docker and deployed on a Kubernetes cluster. Under normal conditions, three Gunicorn server replicas handle requests. When traffic increases, the HPA detects rising CPU utilisation and automatically launches additional replicas, up to ten if necessary.

Deploying a new version is done via a rolling update: Kubernetes progressively replaces old pods with new ones, verifying each new pod's health before removing the old one. If health checks fail, the deployment is automatically rolled back. A CDN in front of the Kubernetes cluster serves static assets, reducing load on application pods.

Implementation

1. Assess the need: Kubernetes brings significant operational complexity. Make sure your project genuinely requires automatic scaling, high availability, or multi-node deployment before adopting it.
2. Choose a managed service: rather than installing Kubernetes yourself, use a managed service like EKS (AWS), AKS (Azure), or GKE (Google Cloud). The control plane is managed by the provider, reducing operational burden.
3. Containerise the application: ensure your application is properly containerised with Docker and works well with Docker Compose before moving to Kubernetes.
4. Write manifests: define your Deployments, Services, Ingress, and ConfigMaps in YAML. Use Helm to package and version these manifests.
5. Set up monitoring: deploy Prometheus and Grafana to monitor cluster and application metrics. Configure alerts for critical situations.
6. Implement CI/CD: integrate Kubernetes deployment into your CI/CD pipeline with tools like ArgoCD or Flux for a GitOps workflow.

Associated Technologies and Tools

• Docker: container runtime used by Kubernetes to execute pods.
• Helm: package manager for Kubernetes, simplifying complex application deployment.
• Prometheus / Grafana: monitoring and visualisation stack for Kubernetes clusters.
• Nginx Ingress: the most popular HTTP ingress controller for Kubernetes.
• Terraform: declarative provisioning of cloud infrastructure and Kubernetes clusters.

Conclusion

Kubernetes is the reference container orchestration platform for large-scale production deployments. Its ability to automate scaling, deployments, and failure management makes it a powerful tool for critical applications. However, its complexity is not negligible and is not justified for every project. At Kern-IT, we take a pragmatic approach: we recommend Kubernetes when availability, scalability, or multi-cloud constraints demand it, and favour simpler solutions (Docker, Nginx, Gunicorn, Fabric) for projects where this complexity adds no value. The key is choosing the right tool for the actual need.