Course Structure: Linux Systems and DevOps Engineering...
Course Structure: Linux Systems and DevOps Engineering
Module 1: Foundations of DevOps & Advanced Git
→ Advanced Data Structures → Time & Space Complexity, Amortized Analysis→ Trees & Graphs→ Introduction to DevOps:
→ What is DevOps? History, principles (CALMS), and culture.→ The DevOps toolchain and lifecycle.→ Transitioning from traditional IT to a DevOps model.→ Shell Scripting using Bash
→ Writing Scripts, Shell Scripting Functions, Scripting for System and Network Administrators→ Programs and Project integration using make, makefile→ Python for DevOps & Automation
→ Basic Language for writing complex automation scripts,→ Lambda/Cloud Functions, and interacting with cloud APIs.→ SCCS (Advanced):
→ Source Code Control System.→ Architecture, Concurrency, File-by-file control, Scope, History storage, Branching.→ Interleaved deltas, Exclusive locking.→ Version identification, Keyword expansion.→ Apache Subversion (SVN)
→ Update, Commit
→ Centralized Model, Working Copy, Revisions.
→ Trunk, Branches, and Tags, Atomic Commits
→ Git (Advanced):
→ Review of core Git commands.→ Advanced Branching Strategies: GitFlow, GitHub Flow, GitLab Flow.→ CI/CD Workflows: The role of Git in automated pipelines.→ Git hooks and managing large repositories.Module 2: Containerization with Docker
→ Docker Fundamentals:
→ Containers vs. virtual machines.→ Docker Engine: Architecture and components.→ Images & Containers: Building images with Dockerfiles, managing containers.→ Docker Networking: Bridge, host, and user-defined networks.→ Docker Volumes: Persistent storage for containers.→ Docker in Practice:
→ Multi-stage builds for smaller images.→ Docker Compose: Defining and running multi-container applications.→ Introduction to Orchestration: The need for a container orchestrator.Module 3: Orchestration with Kubernetes (K8s)
→ Kubernetes Core Concepts:
→ Cluster Architecture: Master and worker nodes.→ Basic Primitives: Pods, Deployments, Services, and Namespaces.→ ReplicaSets and scaling applications.→ ConfigMaps & Secrets for configuration management.→ Ingress for external access.→ Advanced Kubernetes:
→ Persistent Volumes & Claims for stateful applications.→ Helm: Packaging and managing Kubernetes applications.→ Troubleshooting: Log analysis, pod and service debugging.Module 4: CI/CD Pipelines & Automation
→ CI/CD Philosophy:
→ The importance of continuous integration, delivery, and deployment.→ Best practices for building automated pipelines.→ CI/CD with GitHub Actions/GitLab CI:
→ GitHub Actions: Creating and managing workflows, jobs, and steps.→ GitLab CI: Using the .gitlab-ci.yml file, runners, and stages.→ Integrating security scanning (SAST/DAST) into pipelines.→ Project: Deploying a Microservices App on Kubernetes Cluster
→ Trainees will use Git to manage code for a multi-service application.→ They will write Dockerfiles for each service.→ They will create a CI/CD pipeline using either GitHub Actions or GitLab CI.→ The pipeline will automatically build Docker images, push them to a container registry, and deploy the application to a local Kubernetes cluster.→ They will use Kubernetes YAML manifests to define deployments and services.Module 5: Cloud & Infrastructure as Code (IaC)
→ Advanced Networking→ Service Mesh (Istio/Linkerd)→ For microservices, a service mesh handles advanced traffic management (Canary, Blue/Green), security, and observability.→ Cloud Fundamentals (AWS/GCP):
→ Introduction to AWS (EC2, S3, VPC, IAM) or GCP (Compute Engine, Cloud Storage, VPC).→ Shared Responsibility Model in the cloud.→ Linux instances in the cloud and security groups.→ Infrastructure as Code (IaC) with Terraform:
→ Why IaC? Benefits and principles.→ Terraform Basics: State file, providers, resources.→ Writing HCL (HashiCorp Configuration Language) to provision infrastructure.→ Managing cloud resources using Terraform.→ Container Orchestration on Cloud:
→ Amazon EKS (Elastic Kubernetes Service) or Google GKE (Google Kubernetes Engine).→ Deploying a production-grade Kubernetes cluster on a major cloud provider.→ Integrating the cluster with other cloud services.Module 6: Reliability, Monitoring, Observability & Advanced Topics
→ System Reliability
→ SRE Principles: Concepts like SLOs, SLAs, SLIs, Error Budgets, and Postmortems are the backbone of modern operations and bridge the gap between DevOps and SRE.
→ Monitoring & Observability:
→ Distinction between monitoring, logging, and tracing.→ Prometheus & Grafana: Setting up a monitoring stack for Kubernetes.→ ELK Stack (Elasticsearch, Logstash, Kibana) for centralized logging.→ Security in DevOps (DevSecOps):
→ Vulnerability Scanning & Policy→ Integration of tools like Trivy (for image scanning) or basic security checks in the CI/CD pipeline is non-negotiable for modern DevOps.→ Shifting security left.→ Integrating security into the CI/CD pipeline.→ Configuration Management (Intro to Ansible):
→ Ansible/Puppet/Chef (In-depth)→ Managing Virtual Machines, Patching, and pre/post-deployment tasks outside of Kubernetes.→ Automating system configuration tasks.→ Simple playbooks for server setup.→ Cost Management→ FinOps in the Cloud: Understanding cloud cost optimization, identifying unused resources, and rightsizing instances is a critical, high-value skill for any cloud role.→ Troubleshooting Tools→ Linux Utility Deep Dive: Leveraging the trainees' Linux background to master advanced tools like strace, lsof, tcpdump, and advanced netstat/ss for deep system/container debugging.Final Project: Cloud Deployment with Monitoring & Auto-scaling
→ Project Goal: To create a production-like environment for a web application.
→ Steps:
1. Use Terraform to provision a VPC, subnets, and an EKS/GKE cluster in AWS/GCP.
2. Containerize the application and set up a CI/CD pipeline to build and push images to a cloud registry.
3. Deploy the application to the cloud Kubernetes cluster using Kubernetes manifests.
4. Set up Prometheus and Grafana to monitor the cluster and application metrics.
6. Bonus: Add a simple health check or a canary deployment to the CI/CD pipeline.