In this post, let’s check 5 Kubernetes mini-projects that use a variety of resources and offer unique scenarios to make better understanding (where to use Kubernetes?)

Project 1: Scalable Web Application

  • Requirement: Create a highly available website that can handle varying traffic loads.
  • Summary: Deploy a containerized web application, use load balancing for distribution, and configure autoscaling rules to handle surges in requests.
  • Tools/Services:
    • Docker (or another container engine) for building container images.
    • Kubernetes Services for load balancing.
    • Horizontal Pod Autoscalers for automatic scaling.

Project 2: Deploy a Self-Healing Microservice

  • Requirement: Ensure a microservice is always available, even in case of pod failures.
  • Summary: Deploy a microservice with multiple replicas, utilize health checks (liveness and readiness probes), and set up automatic redeployment of failed pods.
  • Tools/Services:
    • Docker for building images.
    • Kubernetes Deployments to manage multiple replicas.
    • Kubernetes Liveness & Readiness probes for health monitoring.

Project 3: Database with Persistent Storage

  • Requirement: Deploy a database that retains data even if pods are rescheduled.
  • Summary: Provision a database (e.g., PostgreSQL, MySQL), and attach persistent storage to hold data securely.
  • Tools/Services:
    • Docker for containerizing the database.
    • Kubernetes Persistent Volumes & Persistent Volume Claims for storage management.
    • A database of your choice.

Project 4: CI/CD Pipeline with Jenkins

  • Requirement: Automate build, test, and deployment of a containerized app.
  • Summary: Set up Jenkins within Kubernetes and create a pipeline that pulls code changes, builds a new container image, tests it, and deploys the update to an environment.
  • Tools/Services:
    • Docker for building container images.
    • Jenkins deployed as a Kubernetes Pod.
    • A source code repository (e.g., GitLab, GitHub).

Project 5: Centralized Logging with Fluentd

  • Requirement: Collect and store log data from all the containers in your Kubernetes cluster.
  • Summary: Deploy Fluentd as a DaemonSet to gather logs from containers, and ship them to a storage and analysis platform like Elasticsearch.
  • Tools/Services:
    • Fluentd deployed in Kubernetes.
    • Elasticsearch (can be outside Kubernetes) for log storage and analysis.
    • Kibana (optional) for log visualization.

Important Considerations:

  • Start Small: Begin with basic versions of each project and gradually increase complexity.
  • Cloud/Local Setup: You can use cloud-based Kubernetes clusters (AWS EKS, GCP GKE, Azure AKS) or local solutions like Minikube for experiments.
  • Focus on Concepts: While tools are important, emphasize understanding core Kubernetes resources (Deployments, Services, ConfigMaps, Secrets, etc.) within each project.

Terraform : 5 Mini Projects to get Hands-on

Prometheus and Grafana (5 bite size Projects)

Hope you find this post helpful.




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