Kartikeya Soft

Kubernetes is a powerful open-source platform for managing containerized applications. Containers act as self-contained units, each with its own storage, network, and processing power. Kubernetes excels at orchestrating these containers, ensuring they run efficiently and adapt to changing needs.

This course will equip you with the knowledge to harness Kubernetes's potential. We'll explore its key features, the benefits it offers, and how to deploy and monitor containerized applications using this industry-leading tool.

Containerization : Docker

1: Introduction to Docker

Understanding the need for containerization
Introduction to Docker and its ecosystem
Docker architecture overview
Installing Docker on various platforms

2: Working with Docker Containers

Creating and running containers from Docker images
Managing container lifecycle: start, stop, pause, and delete
Inspecting container configurations and logs
Sharing and distributing Docker images using Docker Hub and Docker Registry

3: Docker Images

Understanding Docker images: layers, tags, and manifests
Building Docker images using Dockerfile
Best practices for writing efficient Dockerfiles
Versioning and tagging Docker images

4: Docker Networking

Understanding Docker networking modes: bridge, host, overlay
Managing network interfaces and ports for Docker containers
Connecting containers together using Docker networks

5: Docker Volumes and Data Management

Understanding Docker volumes and persistent data storage
Managing volumes for data persistence and sharing
Backing up and restoring Docker volumes
Implementing data management strategies for Dockerized applications

6: Docker Security

Understanding Docker security best practices
Container isolation and privilege escalation prevention
Securing Docker daemon and API endpoints
Implementing image scanning and vulnerability management

7: Docker in Production

Designing Docker-based architectures for production environments
Implementing high availability and fault tolerance with Docker
Monitoring Docker containers and clusters
Deploying Docker-based applications to cloud platforms

8: Dockerizing Applications

Dockerizing various types of applications: web servers, databases, microservices
Best practices for containerizing legacy applications
Optimizing Docker images and containers for performance and resource efficiency
Testing and debugging Dockerized applications

Docker Deployment

Design and deploy a real-world application using Docker
Implement security, networking, and data management best practices
Present and demonstrate the Docker deployment to peers

Kubernetes

Module 1: Introduction to Kubernetes

Understanding containerization
Evolution of container orchestration
Introduction to Kubernetes
Key concepts: Pods, Nodes, Services, Deployments

Module 2: Kubernetes Architecture

Master node components: API server, Controller manager, Scheduler, etcd
Worker node components: Kubelet, Kube-proxy, Container runtime
Understanding the control plane and data plane

Module 3: Deploying Applications in Kubernetes

Creating and managing Pods
Configuring Services for networking
Handling application scaling with Deployments
Managing application lifecycle with ReplicaSets

Module 4: Kubernetes Networking

Understanding Kubernetes networking model
Container-to-container communication within Pods
Services for exposing applications internally and externally
Ingress controllers and load balancing

Module 5: Kubernetes Storage

Understanding storage options in Kubernetes
Persistent Volume (PV) and Persistent Volume Claim (PVC)
Storage classes and dynamic provisioning
StatefulSets for managing stateful applications

Module 6: Kubernetes Security

Authentication and authorization in Kubernetes
Role-based access control (RBAC)
Network policies for controlling traffic between Pods
Securing container images and runtime

Module 7: Advanced Kubernetes Features

DaemonSets for running a copy of a Pod on all or selected nodes
Jobs and CronJobs for batch processing and periodic tasks
Custom Resource Definitions (CRDs) and Operators for extending Kubernetes

Module 8: Kubernetes Best Practices

Best practices for designing and deploying applications in Kubernetes
Optimizing resource utilization and efficiency
Disaster recovery and high availability strategies
Upgrading and maintaining Kubernetes clusters

Module 9: Hands-On Projects and Case Studies

Building and deploying real-world applications in Kubernetes
Troubleshooting common issues and debugging techniques
Case studies of Kubernetes adoption in various industries

Module 10: Introduction to Amazon EKS:

Understand the functionalities of Amazon EKS as a managed Kubernetes service.
Learn how EKS integrates with other AWS services for storage, networking, and security.
Explore the pricing model and different deployment options for EKS clusters.

Module 11: Building and Managing EKS Clusters:

Learn how to create and configure EKS clusters using the AWS Management Console, AWS CLI, or eksctl tool.
Understand different node types and scaling strategies for EKS clusters.
Explore security best practices for deploying applications in Amazon EKS.

Module 12: Deploying Applications to EKS:

Learn how to deploy containerized applications to EKS clusters using deployments and pods.
Understand how to manage application configurations and secrets using Kubernetes manifests.
Explore techniques for rolling updates and scaling your applications.

Module 13: Introduction to Azure Kubernetes Service (AKS):

Understand the functionalities of AKS as a managed Kubernetes service on Microsoft Azure.
Learn how AKS integrates with other Azure services for storage, networking, and security.
Explore the pricing model and different deployment options for AKS clusters.

Module 14: Building and Managing AKS Clusters:

Learn how to create and configure AKS clusters using the Azure Portal, Azure CLI, or kubectl tool.
Understand different node types, virtual machine options, and scaling strategies for AKS clusters.
Explore security best practices for deploying applications in Azure Kubernetes Service.

Module 15: Deploying Applications to AKS:

Learn how to deploy containerized applications to AKS clusters using deployments and pods.
Understand how to manage application configurations and secrets using Kubernetes manifests or Azure Container Registry (ACR).
Explore techniques for rolling updates, scaling your applications, and health probes.

Module 16: Introduction to Google Kubernetes Engine (GKE):

Understand the functionalities of GKE as a managed Kubernetes service on Google Cloud Platform (GCP).
Learn how GKE integrates with other GCP services for storage, networking, and security.
Explore the pricing model and different deployment options for GKE clusters (Standard, Autopilot, etc.).

Module 17: Building and Managing GKE Clusters:

Learn how to create and configure GKE clusters using the Google Cloud Console, Google Cloud SDK (gcloud), or kubectl tool.
Understand different node types, machine types, and scaling strategies for GKE clusters.
Explore security best practices for deploying applications in Google Kubernetes Engine.

Module 18: Deploying Applications to GKE:

Learn how to deploy containerized applications to GKE clusters using deployments and pods.
Understand how to manage application configurations and secrets using Kubernetes manifests or Google Container Registry (GCR).
Explore techniques for rolling updates, scaling your applications, and health checks.

Final Project: Kubernetes Deployment

Design and deploy a multi-tier application in Kubernetes
Implement best practices for security, monitoring, and scalability
Present and showcase the deployment to peers

Terraform

Module 1: Terraform Fundamentals

Installing Terraform
Terraform Workflow
Providers
Resources
Variables
Outputs
Local Values
Expressions

Module 2: Production Terraform

Modules
Remote State
State Locking
Workspaces
Backend Configuration

Module 3: Kubernetes Infrastructure

Deploy

* AKS

* EKS

* GKE

using Terraform

Helm Chart

Module 1: Introduction to Helm and Kubernetes:

What is Helm?
Why Helm is used in Kubernetes?
Basic concepts of Kubernetes (Pods, Deployments, Services, etc.).

Module 2: Installation and Setup:

Installing Helm on different platforms (Linux, macOS, Windows).
Configuring Helm and initializing Helm on a Kubernetes cluster.

Module 3: Understanding Helm Charts:

What is a Helm chart?
Anatomy of a Helm chart (Chart.yaml, values.yaml, templates/, etc.).
Versioning and managing dependencies.

Module 4: Creating Helm Charts:

Setting up a Helm chart structure.
Defining Kubernetes resources using YAML templates.
Parameterizing Helm charts using values.yaml and templating.

Module 5: Managing Releases with Helm:

Installing, upgrading, and deleting Helm releases.
Understanding Helm release lifecycle and rollback mechanisms.
Managing releases across different environments (dev, staging, production).

Module 6: Hands-on Labs and Projects:

Building and deploying applications using Helm charts.
Troubleshooting common issues with Helm deployments.
Creating reusable Helm charts for common application patterns.

Module 7: Helm in Production :

Scalability and performance considerations.
Monitoring and logging Helm-managed applications.
Disaster recovery and backup strategies.

Prometheus

Module 1: Introduction to Monitoring:

Overview of monitoring concepts and importance.
Understanding the need for monitoring in modern distributed systems.
Introduction to Prometheus as a monitoring and alerting toolkit.

Module 2: Prometheus Architecture:

High-level architecture of Prometheus.
Components: Prometheus server, exporters, push gateway, alert manager.
Data model: time series data, labels, metrics, and targets.

Module 3: Installing and Configuring Prometheus:

Installation methods: binary, package managers, Docker, Kubernetes.
Configuration options: scrape configs, service discovery, storage, retention policies.
Basic configuration examples for common use cases.

Module 4: Collecting Metrics with Prometheus:

Instrumenting applications with client libraries (e.g., Prometheus client for Go).
Exporters: Understanding and using Prometheus exporters for third-party systems.
Best practices for exposing metrics and instrumenting applications effectively.

Module 5: Querying Metrics with PromQL:

Introduction to PromQL (Prometheus Query Language).
Basic querying: selecting, filtering, aggregating time series data.
Advanced querying techniques: rate, increase, quantiles, functions, and operators.

Module 6: Alerting with Prometheus:

Introduction to alerting rules and alerting pipeline.
Defining alerting rules with Prometheus DSL.
Configuring alert manager: routing, inhibition, receivers.
Best practices for writing effective alerting rules and managing alerts.

Module 7: Visualization and Dashboards:

Integrating Prometheus with Grafana for visualization.
Building dashboards: configuring panels, queries, and visualizations.
Best practices for designing effective monitoring dashboards.

Module 8: Hands-on Labs and Projects:

Setting up Prometheus and Grafana in a local environment.
Instrumenting applications and collecting custom metrics.
Creating alerting rules and configuring alert managers.
Building custom dashboards and visualizations.

Grafana

Module 1: Introduction to Grafana:

Overview of Grafana as an open-source analytics and visualization platform.
Importance and benefits of using Grafana for monitoring, metrics visualization, and analytics.

Module 2: Installation and Setup:

Installing Grafana on different platforms (Linux, Windows, macOS).
Configuring Grafana for the first time: setting up data sources, users, and organizations.

Module 3: Understanding Grafana Concepts:

Data sources: Introduction to different data sources supported by Grafana (Prometheus, Graphite, InfluxDB, Elasticsearch, etc.).
Dashboards: Understanding dashboards, panels, and visualizations.

Module 4: Building Dashboards:

Creating dashboards from scratch: adding panels, configuring queries, and visualizations.
Exploring different visualization options: graphs, tables, heatmaps, single stats, etc.
Customizing dashboards: annotations, time ranges, variables, and annotations.

Module 5: Advanced Dashboard Techniques:

Using plugins to extend Grafana's functionality.
Dashboard version control and collaboration using Git.
Best practices for designing and organizing dashboards.

Module 6: Grafana in Production:

Scaling Grafana for high availability and performance.
Security best practices: authentication, authorization, SSL/TLS.
Backup and disaster recovery strategies.

GitOps And ArgoCD

Module 1: Introduction to GitOps:

Overview of GitOps principles and practices.
Understanding the benefits of GitOps for continuous delivery and infrastructure management.
Contrasting GitOps with traditional deployment methodologies.

Module 2: Introduction to ArgoCD:

Overview of ArgoCD as a GitOps continuous delivery tool.
Features and capabilities of ArgoCD.
Understanding the architecture and components of ArgoCD.

Module 3: Installation and Setup:

Installing ArgoCD on different platforms (Kubernetes, Docker, etc.).
Configuring ArgoCD for the first time: setting up repositories, applications, and sync strategies.

Module 4: Managing Applications with ArgoCD:

Creating applications in ArgoCD from Git repositories.
Defining application specifications using GitOps manifests (e.g., YAML).
Continuous deployment with ArgoCD: automatic synchronization and rollback strategies.

Module 5: Declarative Configuration with Git:

Leveraging Git as the source of truth for infrastructure and application configuration.
Using Git workflows (branches, pull requests, etc.) for managing changes.
Applying GitOps principles to version control infrastructure changes.

Module 6: Monitoring and Observability:

Integrating ArgoCD with monitoring tools (Prometheus, Grafana, etc.).
Setting up alerts and notifications for deployment events.
Observing application health and performance with ArgoCD.

Module 7: Advanced GitOps Workflows:

Implementing advanced GitOps workflows: Canary deployments, Blue/Green deployments, etc.
Multi-cluster and multi-environment deployments with ArgoCD.
Integrating testing and validation into GitOps pipelines.

Module 8: GitOps Best Practices and Case Studies:

Best practices for designing effective GitOps workflows.
Real-world case studies and use cases of GitOps in production environments.
Lessons learned and common pitfalls to avoid.

Module 9: Hands-on Labs and Projects:

Setting up GitOps pipelines with ArgoCD.
Deploying and managing applications using GitOps principles.
Implementing advanced deployment strategies with ArgoCD.

Become a Production-Ready Kubernetes Engineer

🏦 Enterprise Production Projects
🧪 Real-Time Hands-On Labs
🏆 CKA Exam Preparation
🏆 CKAD Exam Preparation
📝 Kubernetes Mock Tests
💼 Resume & Portfolio Building
🎤 Technical Interview Preparation
🚀 Production Troubleshooting Practice
🎯 Career Guidance & Job Readiness

Don't just learn Kubernetes from slides or tutorials—gain practical experience by working on enterprise-style projects, solving production scenarios, and using the tools that modern DevOps teams rely on every day.

Whether you're a fresher starting your journey or an experienced IT professional looking to level up, this course is designed to help you build valuable skills and confidence.

🌈 Your next opportunity starts with the skills you build today.✨