Pre Requisite Kubernetes Introduction

Welcome — in this lesson we introduce container orchestration and why it matters for running containers at production scale. My name is Michael Levan. We already learned how to package applications into Docker images. The next step is running and managing those containers reliably across many machines. This is where container orchestration comes in. Common operational questions orchestration answers:

How do you run applications that depend on other containers (databases, message brokers, backend services)?
How do you scale an application as traffic increases, then scale down when it drops?
How do you coordinate networking and service discovery across many containers?
How do you recover automatically from hardware or container failures?

Container orchestration is the platform layer that manages a cluster of machines, schedules containers across nodes, maintains connectivity between services, and automatically scales or heals workloads based on policy and load. This automatic deployment and management of containers is commonly called container orchestration.

A slide titled "Container Orchestration" showing three user icons above four rectangular host boxes. Inside the host boxes are small container symbols linked by dashed lines, with a blue "Orchestration" bar along the bottom.

Orchestration technologies: a quick overview

Kubernetes is the most widely used container orchestration platform, but there are alternatives you may encounter:

Docker Swarm: quick to set up and easy for small deployments; less feature-rich for complex production needs.
Apache Mesos: very powerful and flexible, but typically more complex to install and operate.
Kubernetes: steeper learning curve and more involved to set up, but offers extensive features, extensibility, and broad cloud and vendor support.

A presentation slide titled "Orchestration Technologies" showing three blue panels with the logos and names of Docker Swarm (left), Kubernetes (center), and Mesos (right). Each panel uses blue tones and the respective service icon.

Below is a concise comparison to help you choose the right tool depending on your needs.

Technology	Best for	Notes
Docker Swarm	Small teams, simple setups	Fast to get started, limited advanced features
Apache Mesos	Large, complex, heterogeneous workloads	Highly flexible but operationally complex
Kubernetes	Production-grade clusters, cloud-native apps	Rich feature set, strong ecosystem and cloud support

Kubernetes is supported across major public cloud providers (Google Cloud, Azure, AWS) and consistently ranks among top open-source projects. Its ecosystem — including tools for CI/CD, monitoring, and networking — makes it a natural choice for many production deployments.

Why choose Kubernetes? Key advantages

Kubernetes provides several capabilities that are especially valuable for production systems:

High availability: run multiple instances of your app across nodes so hardware or node failure doesn’t take your service down.
Load balancing: distribute user traffic across containers to improve responsiveness.
Elastic scaling: scale services up and down automatically in response to load.
Node autoscaling: add or remove underlying machines without downtime.
Declarative management: describe the desired state in YAML/JSON manifests and let Kubernetes converge the cluster to that state.

A slide titled "Kubernetes Advantage" showing a cloud orchestration icon connecting users to multiple Kubernetes pods/containers. Each container is depicted with Web and Backend service blocks to illustrate distributed app deployment.

Example: prefer declarative workflows for production deployments. Create a resource file and apply it:

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
        - name: my-app
          image: my-org/my-app:latest

Apply with:

kubectl apply -f deployment.yaml

Or use an imperative command for quick tests:

kubectl run nginx --image=nginx --replicas=2

Start with small, practical experiments: run a single-node cluster (like Minikube or Kind), deploy a simple app, then add a service and a scaling policy. Hands-on practice helps solidify concepts like pods, deployments, and services.

Kubernetes is powerful but operationally complex. For production, plan for monitoring, backups, security (RBAC, network policies), and cluster lifecycle management. Consider managed Kubernetes offerings (EKS, AKS, GKE) if you want to offload control plane operations.

Next steps and references

You’ll learn Kubernetes architecture and core primitives (pods, services, deployments, replica sets, StatefulSets) in upcoming lessons. For further reading and official docs:

That is all for this lesson. Thank you for reading, and I will see you in the next lesson.

Introduction

Pre-Requisite – Docker and Kubernetes

Getting Started with OpenShift

OpenShift Concepts – Projects and Users

Concepts – Builds and Deployments

Networks, Services, Routes and Scaling

Storage, Templates and Catalog

OpenShift Security

Conclusion and APPENDIX

Pre Requisite Kubernetes Introduction

Orchestration technologies: a quick overview

Why choose Kubernetes? Key advantages

Next steps and references

Watch Video

​Orchestration technologies: a quick overview

​Why choose Kubernetes? Key advantages

​Next steps and references

Watch Video

Orchestration technologies: a quick overview

Why choose Kubernetes? Key advantages

Next steps and references