Kubernetes is not just YAML — it is a distributed systems platform for running containerized workloads reliably at scale.
This course focuses on how Kubernetes behaves, why it exists, and how to reason about it operationally — especially if you already understand Docker.
🎯 Course Goals
By the end of this course, you will be able to:
- Understand Kubernetes core primitives and their responsibilities
- Reason about scheduling, scaling, and self-healing
- Debug real-world Kubernetes issues methodically
- Connect Docker mental models to Kubernetes abstractions
📘 Modules (Planned)
1️⃣ Kubernetes Architecture & Mental Model
- Why Kubernetes exists
- Control plane vs worker nodes
- API server, scheduler, controller manager
- Declarative desired state
2️⃣ Pods & Workloads
- Pods and containers
- Deployments, ReplicaSets, Jobs, CronJobs
- Rolling updates and restarts
- Restart and failure behavior
3️⃣ Networking & Services
- Cluster networking basics
- Services (ClusterIP, NodePort, LoadBalancer)
- DNS inside the cluster
- Ingress and traffic flow (high level)
4️⃣ Configuration & Secrets
- ConfigMaps vs Secrets
- Environment variables vs mounted files
- Configuration patterns that scale
5️⃣ Storage & Persistence
- Volumes in Kubernetes
- PersistentVolumes and PVCs
- Stateful workloads basics
6️⃣ Scaling & Reliability
- Horizontal Pod Autoscaling
- Resource requests and limits
- Readiness vs liveness probes
- Self-healing behavior
7️⃣ Debugging & Operations
- Inspecting Pods and workloads
- Logs and exec
- Common failure modes
- Mapping Docker debugging skills to Kubernetes
🧭 Navigation
- 🚧 This course is coming soon
- 👉 Recommended prerequisite: Docker Mastery
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