Mastering Kubernetes Migrations From Planning to Execution
Preparing to migrate workloads to Kubernetes can be daunting from a technical and operational perspective. The transition isn’t just about getting Kubernetes up and running; it also requires establishing a foundation for long-term success.
For a smooth Day 0 migration, platform engineers must address several key factors, including security, application deployment, CI/CD alignment, and tooling choices to ensure their Kubernetes fleets are reliable, high-performance, and manageable over time.
Let’s explore these requirements in detail.
Laying the Technical Foundation
Before diving into workloads, it’s critical to establish a solid Kubernetes environment. Choosing the correct Kubernetes distribution is an early decision that can impact future operations. Managed services like Amazon EKS, GKE, and AKS simplify cluster operations, while self-hosted solutions may provide greater control but require more operational overhead. Also, cluster architecture planning should consider availability zones, autoscaling, and storage persistence.
Equally important is preparing teams for the shift to a cloud native mindset. Kubernetes isn’t just a new platform; it requires a fundamental change in how applications are built, deployed, and maintained. Engineers accustomed to traditional infrastructure must adapt to declarative management, containerized workloads, and dynamic orchestration. Investing in hands-on training, certifications, and internal knowledge-sharing sessions can accelerate this transition. Organizations should also foster a culture of continuous learning, encouraging teams to experiment with Kubernetes-native patterns like GitOps, service meshes, and progressive delivery models. Without this shift in mindset, even the most technically sound Kubernetes deployment can struggle with operational inefficiencies and adoption challenges.
Choosing the Right Applications for Kubernetes
Not every workload is a natural fit for Kubernetes, and not every migration follows a straightforward path from a monolith to containers. Before committing to a Kubernetes migration, organizations should evaluate whether containerization aligns with the application’s architecture, performance needs, and operational goals.
Applications with unpredictable traffic patterns, microservices-based architectures, or those requiring rapid scaling benefit the most from Kubernetes. However, highly latency-sensitive workloads, tightly coupled legacy applications, and software with complex licensing dependencies may not see significant gains from migration. Some applications may be better suited for alternative modernization approaches, such as serverless computing or retaining traditional virtualized environments.
For those moving forward with Kubernetes, determining which applications should be containerized first is the next step. Stateless services are the easiest to migrate, requiring minimal changes and efficiently scaling using Kubernetes Deployments. On the other hand, stateful applications require careful planning to handle data persistence and consistency, often leveraging StatefulSets and Persistent Volume Claims (PVCs).
Additionally, dependencies must be assessed — some applications may need to be re-architected to decouple from legacy services before migration. Workloads that rely on shared file systems, legacy middleware, or traditional session management may require additional refactoring to function effectively in a Kubernetes-native environment. A hybrid approach may be the best solution in some cases, where specific components remain on VMs or bare metal while others migrate to Kubernetes.
Successful migration is not just about lifting and shifting workloads; it’s about identifying the right workloads, understanding their dependencies, and selecting a strategy that balances modernization with operational efficiency.
Setting Up a Secure Cluster
Security in Kubernetes is an ongoing process, but Day 0 is when foundational guardrails must be implemented. RBAC should be enforced at the namespace level, with fine-grained permissions assigned to workloads and users. Network segmentation using Kubernetes Network Policies and enforcing mutual TLS (mTLS) via service meshes can prevent unauthorized lateral movement. To automate security and policy enforcement, consider Kyverno or OPA Gatekeeper. Meanwhile, network security tools like Cilium (leveraging eBPF) can offer advanced protection beyond standard Kubernetes Network Policies.
Kubernetes API access should also be locked down by disabling unused APIs and collecting audit logs using Fluentd or the Kubernetes-native Audit Logging feature. Meanwhile, ingress should be secured with Web Application Firewalls (WAFs) and API gateways like Kong or Ambassador to filter and authenticate requests before they reach backend services.
Logging and monitoring are crucial for long-term visibility. To ensure full observability across workloads, consider using Prometheus and Grafana for performance monitoring, Loki for log aggregation, and OpenTelemetry for tracing. Security monitoring should also include runtime protection with Falco and anomaly detection via Kubernetes Security Posture Management (KSPM) solutions.
Aligning CI/CD Workflows With Kubernetes
Kubernetes introduces new deployment models that may require adjustments to existing CI/CD workflows, but that doesn’t mean traditional pipelines are incompatible. While Kubernetes’ declarative nature lends itself well to GitOps, where tools like ArgoCD and Flux maintain version-controlled cluster states, many organizations successfully integrate Kubernetes with conventional CI/CD pipelines such as Jenkins, GitLab CI/CD, and CircleCI.
The key is ensuring that deployments align with Kubernetes’ model of managing infrastructure and application state declaratively. Traditional CI/CD pipelines can be adapted by incorporating Kubernetes manifests, Helm charts, or Kustomize to define application configurations. Some teams opt for a hybrid approach, where GitOps manages infrastructure and application releases, while traditional pipelines handle build, testing, and artifact management.
Ultimately, the right approach depends on an organization’s existing tooling, operational maturity, and security requirements. Whether using GitOps, traditional CI/CD, or a combination of both, the focus should be on ensuring reliability, consistency, and seamless deployments across environments.
Progressive delivery strategies should be integrated into deployment processes to reduce the impact of faulty rollouts and enhance resilience. Service meshes can help facilitate traffic shifting and gradual rollouts. Also, consider Canary releases to gradually expose new versions to a small subset of users, blue-green deployments to maintain two live environments that allow for instant rollbacks, and feature flagging to enable or disable features dynamically without redeploying.
Troubleshooting and Health Management in Kubernetes
A successful Kubernetes migration doesn’t end with deployment — it requires ongoing monitoring, troubleshooting, and proactive health management to ensure stability. Kubernetes introduces a complex, dynamic environment where workloads are constantly scheduled, rescheduled, and autoscaled. Identifying and resolving issues can be challenging without proper visibility and diagnostic tools.
Proactive Monitoring and Observability
Real-time observability is critical for detecting performance bottlenecks, resource constraints, and failing workloads. Kubernetes-native tools like Prometheus (for metrics), Loki (for logs), and OpenTelemetry (for distributed tracing) provide deep visibility into cluster health. Dashboards built with Grafana help teams quickly assess system performance and identify anomalies.
Diagnosing and Resolving Issues
Troubleshooting in Kubernetes often requires digging into multiple layers, from application logs to pod events and cluster-wide configurations. Tools like kubectl describe and kubectl logs provide basic insights, while more advanced solutions like Komodor and Lens aggregate logs, events, and configurations into a single interface for faster diagnosis. Kubernetes’s built-in liveness and readiness probes help identify failing containers and restart them automatically.
Automated Remediation and Self-Healing
Kubernetes supports self-healing capabilities through built-in mechanisms such as ReplicaSets and StatefulSets, which automatically reschedule failed pods. Horizontal Pod Autoscalers (HPA) adjust resources based on workload demand, while tools like KEDA extend this functionality for event-driven scaling. In cases where human intervention is needed, AI-powered troubleshooting assistants, such as Komodor’s Klaudia, can provide automated insights and guided remediation steps.
Best Practices
A successful migration requires thoughtful planning and the proper tooling to ensure security, automation, and observability.
Here are five steps to remember:
- Secure from the start with RBAC, network policies, and encrypted secrets (e.g., kube-bench, Kyverno, Open Policy Agent).
- Use lightweight, secure images and scan containers in CI/CD (e.g., Trivy, Clair).
- Automate deployments with GitOps (e.g., ArgoCD, Flux).
- Embed observability with Prometheus (metrics), Loki (logs), and OpenTelemetry (tracing).
- Enable safer rollouts with progressive delivery (e.g., Flagger, Argo Rollouts).
A Kubernetes migration doesn’t end at Day 0. To ensure long-term success, teams should continuously refine security policies, automate scaling strategies, and implement proactive monitoring. This includes regularly testing failover mechanisms, enforcing least-privilege access, and streamlining deployments with GitOps practices. By taking these steps, it’s possible to maintain a resilient, high-performing Kubernetes environment that evolves with an organization’s workloads.
