Top Tech Skills for 2025-26: Fullstack, DevOps, AI Guide

This course provides comprehensive training on Docker containerization and Kubernetes orchestration, covering theoretical concepts and practical implementation across all three major cloud providers (AWS EKS, GCP GKE, and Azure AKS), designed to help developers and DevOps professionals master containerization fundamentals and cloud infrastructure management.

Docker solves environment consistency problems by packaging applications with dependencies. Understanding what problem Docker solves is essential. Architecture includes Daemon, CLI, Images, Containers, and Registries. Dockerfiles define how to build images with proper keywords (FROM, RUN, CMD, ENTRYPOINT). Multi-stage Dockerfiles reduce image size. Commands include build, run, stop, start, push, volume, and network management. Docker Compose enables multi-container applications. Network types (bridge, host, overlay) and volume types (named volumes, bind mounts) are important. Kubernetes orchestrates containerized applications across multiple hosts. Understanding what problem Kubernetes solves (automating deployment, scaling, and management) is important. Architecture includes Master Nodes (API Server, Scheduler, Controller Manager, etcd) and Worker Nodes (Kubelet, Kube-proxy). Pod creation workflow involves API Server, Scheduler, and Controller Manager. Resources include Pods, Deployments, Services, ConfigMaps, PersistentVolumes, and StorageClasses. YAML configuration is essential for defining resources. Service types (ClusterIP, NodePort, LoadBalancer) and networking concepts are important. RBAC manages user permissions. Rolling back and auto-scaling (horizontal and vertical) maintain availability. Persistent storage ensures data persistence. Helm charts package applications with configurations.

Containerization (using Docker) enables applications to run in isolated environments sharing underlying infrastructure. Kubernetes orchestrates containerized applications, automatically handling scaling, self-healing, and load balancing. Azure Container Services provides a managed Kubernetes environment, eliminating manual infrastructure management. Benefits include: automatic failure detection and restart, automatic scaling based on demand, and simplified deployment workflows. Organizations can leverage containerization benefits without operational complexity of managing orchestration platforms manually.

Containerization with Docker packages applications with their runtime, libraries, and OS dependencies into lightweight containers, ensuring compatibility across different environments (Windows, Mac, Linux). Container registries like Docker Hub, ECR (AWS), ACR (Azure), and Google Container Registry store container images. Kubernetes is a container orchestration tool that manages containers like a ship captain controls a ship, handling deployment, scaling, load balancing, and self-healing (replacing failed containers).

Containerization with Docker enables portable application deployment. Create .dockerignore files to exclude unnecessary files like compiled binaries and test directories. Multi-stage builds reduce container image size by separating build and runtime stages. Stage 1 (builder) uses a full Go image to compile the application. Stage 2 (runtime) uses a minimal image (like gcr.io/distroless/static) and copies only the compiled binary. This reduces image size from ~948MB to ~26MB. Use COPY --from=builder to transfer artifacts between stages. Docker Compose simplifies multi-container application orchestration. Define services in docker-compose.yml with build context, image name, ports mapping, restart policies, and networks. Use docker compose build to build images, docker compose up to start containers, and docker compose down to stop them. Kubernetes Deployment objects manage containerized application replicas. Define replicas, container image, ports, and labels. Service objects provide network access to pods using selectors matching deployment labels. Apply manifests using kubectl apply -f folder/. Verify deployment with kubectl get pods and services. Services enable load balancing across pod instances. Helm charts package Kubernetes resources for easy deployment. Create charts using helm create <name>. Customize templates (deployment.yaml, service.yaml) and values.yaml for configuration. Use helm template to preview manifests, helm install to deploy, and helm get values to view configuration. Charts enable versioned, templated deployments with configurable parameters like replicas and image tags.

This tutorial demonstrates how to create a decentralized application (DApp) by integrating Hardhat (a development environment for Ethereum) with Ethers.js (a JavaScript library for interacting with the Ethereum blockchain). The process involves setting up a React frontend, deploying a smart contract using Hardhat, creating a contract instance using Ethers.js, and enabling users to read and write data to the blockchain through the web interface.

A fullstack DApp integrates smart contracts (Solidity) with a frontend (React) using libraries like ethers.js, deployed on testnets like Georli and hosted on platforms like Netlify. The development workflow involves writing smart contracts with functions for data storage and fund transfers, deploying them using Hardhat with proper environment configuration, and connecting the frontend to interact with the deployed contract through wallet integration.

Ethers.js is a JavaScript library that serves as a bridge between client-side applications and the Ethereum blockchain, enabling developers to connect to blockchain nodes, read account balances, interact with smart contracts, create transactions, and inspect blocks—all essential skills for building decentralized applications (dApps) in Web 3.0.

This tutorial teaches developers how to build their first decentralized application (dApp) by connecting a simple HTML frontend to a Solidity smart contract using ethers.js, demonstrating the complete workflow from creating a mood diary contract to deploying it on the Ropsten testnet and interacting with it through a web interface.

CI/CD pipelines automate software delivery through two main phases. Continuous Integration (CI) incorporates new code into existing projects through commit, build, test, and artifact generation. Continuous Deployment (CD) delivers artifacts to environments through build, test, and deploy stages. Pipelines handle failures by notifying developers who must correct issues before re-triggering. Each environment (development, staging, production) has different policies based on organizational maturity.

CI/CD (Continuous Integration and Continuous Delivery) automates the software delivery pipeline to accelerate application delivery from months to days. Continuous Integration involves integrating tools and processes before customer delivery, while Continuous Delivery focuses on deploying applications to customer platforms. The standard pipeline includes unit testing (testing individual code blocks), static code analysis (checking formatting and syntax), code quality/vulnerability testing (identifying security risks), end-to-end testing (verifying complete application functionality), reporting (documenting test results), and deployment (making applications accessible). Version Control Systems like GitHub, Bitbucket, and GitLab serve as triggers for automated pipelines, enabling developers to commit changes that automatically trigger testing and deployment workflows.

CI/CD (Continuous Integration, Continuous Delivery, and Continuous Deployment) is a DevOps practice that automates the software delivery process through a pipeline consisting of four stages: Source (code integration), Build (creating deployable artifacts), Test (automated verification), and Deploy (automatic deployment). This automation significantly reduces manual intervention, accelerates time-to-market, improves software quality through early bug detection, and enables frequent, smaller updates with reduced deployment risks. Popular CI/CD tools include Jenkins, GitLab CI, GitHub Actions, Travis CI, AWS CodePipeline, and Azure DevOps.

CI/CD (Continuous Integration/Continuous Deployment) automates the software development process by automatically building, testing, and deploying code changes through a pipeline system. In GitLab CI/CD, pipelines are defined in a .gitlab-ci.yml file and consist of stages (such as Build, Test, Deploy) containing jobs that execute specific commands. To implement CI/CD, developers set up a GitLab Runner on a server, which executes the pipeline commands when code is committed to the repository. This automation ensures that code changes are tested and deployed consistently without manual intervention, preventing errors from reaching production environments.

Jenkins is an open-source automation tool written in Java with plugins for continuous integration. It automates the entire SDLC from code commit to production deployment: pulling code from version control, performing builds, compiling code, validating and reviewing code, performing unit and integration testing, and packaging applications into executable files like WAR or JAR files. Docker is a virtualization environment that creates entire servers in seconds by creating images similar to virtual machines and running them as containers that replicate production environments. This ensures applications behave consistently regardless of deployment location. Together, Jenkins handles the automation workflow while Docker provides consistent deployment environments.