Specialization
DevOps
DevOps is the practice — and the specific tooling — that takes code from a developer's laptop to running reliably in front of real users, automatically, repeatedly, and safely. This module covers the industry-standard toolchain end to end: Docker and Kubernetes for running your app, CI/CD pipelines for shipping it automatically, Terraform and Ansible for managing infrastructure as code, and the monitoring stack that tells you the moment something breaks — plus the culture and practices (SRE, DevSecOps) that tie it all together.
Topic Map
Everything in this module, at a glance
In Depth
Every topic, explained — with real-world industry context
Containerization — Docker
A container packages your app with everything it needs to run, so it behaves identically on your laptop, a teammate's machine, and in production — solving the "works on my machine" problem at its root.
Basics
- What a container actually is — not a lightweight VM, a process with boundaries
- Writing a Dockerfile & building an image
- Running, stopping & inspecting containers
- Images vs. containers vs. registries
Intermediate
- Docker Compose — running multi-container apps locally
- Volumes & persistent data in containers
- Networking between containers
- Multi-stage builds for smaller, production-ready images
Advanced
- Image security scanning & minimizing attack surface
- Optimizing image layers & build cache for fast CI builds
- Container registries in practice (Docker Hub, ECR, GCR, ACR)
- Debugging a container that's crashing in production
Real-World Industry Use Case
Docker is the reason a team can say "if it works in the container, it works in production" — it is the single most foundational tool in this entire module, and every other tool covered here (Kubernetes, CI/CD pipelines) assumes you already know it.
Container Orchestration — Kubernetes
Once you have more than a handful of containers, you need something to schedule them, restart them when they crash, and scale them under load — that's Kubernetes's job.
Basics
- Pods, deployments & services — the core building blocks
- Why Kubernetes exists — the problem of running containers at scale
- kubectl — interacting with a cluster
- Namespaces for organizing resources
Intermediate
- ConfigMaps & Secrets — configuration without rebuilding images
- Ingress — routing external traffic into a cluster
- Scaling — horizontal pod autoscaling
- Helm charts — packaging & templating Kubernetes applications
Advanced
- RBAC — role-based access control inside a cluster
- Managed Kubernetes — AWS EKS, Google GKE & Azure AKS, and how they differ
- StatefulSets & running stateful workloads
- Debugging a real production cluster issue (crash loops, resource limits)
Real-World Industry Use Case
Kubernetes is the dominant container orchestration platform, used in roughly 82% of cloud-native organizations — nearly every company running containers at real scale is running them on Kubernetes, whether self-managed or via EKS/GKE/AKS.
CI/CD Pipelines & Tooling
Continuous Integration and Continuous Deployment automate the path from "I pushed code" to "it's safely running in production" — removing manual, error-prone deploy steps.
Basics
- What CI/CD actually automates, and why it matters
- Writing a basic pipeline — GitHub Actions or GitLab CI
- Running tests automatically on every push
- Build → test → deploy as a single automated flow
Intermediate
- Jenkins — the long-standing industry-standard CI server
- Build tools & artifact management (Maven/Gradle, Nexus/Artifactory)
- Code quality gates (SonarQube) inside a pipeline
- Environment-specific deploys — dev, staging & production pipelines
Advanced
- Pipeline security — secrets management inside CI/CD
- Parallelizing & caching pipelines for speed at scale
- Progressive delivery — feature flags integrated with deploys
- Designing a pipeline for a multi-service, multi-team codebase
Real-World Industry Use Case
Jenkins remains the industry-standard CI server at many established companies, while GitHub Actions has become the default for teams already hosting on GitHub — knowing both, plus the supporting toolchain (SonarQube, Nexus), is exactly what real CI/CD ownership looks like.
Infrastructure as Code & Configuration Management
Instead of manually clicking through a cloud console, Infrastructure as Code lets you define your entire infrastructure in version-controlled files — repeatable, reviewable, and reversible.
Basics
- What Infrastructure as Code solves — no more "it was set up by hand" servers
- Terraform basics — providers, resources & the plan/apply workflow
- Ansible basics — automating configuration across many servers
- Version-controlling infrastructure like application code
Intermediate
- Terraform modules — reusable, composable infrastructure
- Remote state & state locking for team collaboration
- Ansible playbooks & roles for repeatable server configuration
- Provisioning multi-cloud resources with one tool
Advanced
- Managing Terraform state safely across environments (dev/staging/prod)
- Policy as code & guardrails on infrastructure changes
- Alternatives & trade-offs — Pulumi, AWS CloudFormation
- Disaster recovery — rebuilding an entire environment from code
Real-World Industry Use Case
Terraform is the industry-standard IaC tool specifically because it can provision resources across AWS, Azure, and GCP with one consistent workflow — a huge reason companies standardize on it even in multi-cloud environments.
GitOps & Deployment Strategies
GitOps treats your Git repository as the single source of truth for what should be running — and deployment strategies control exactly how new code reaches users without breaking things.
Basics
- What GitOps means — Git as the source of truth for infrastructure & deploys
- Rolling deployments — the default, gradual update strategy
- Rollbacks — safely undoing a bad deploy
- Manual vs. automated deployment approval
Intermediate
- Blue-green deployments — zero-downtime cutovers between two environments
- Canary deployments — shipping to a small percentage of users first
- ArgoCD & Flux — GitOps tools that sync a cluster to a Git repo automatically
- Feature flags as a deployment-risk-reduction tool
Advanced
- Automated rollback triggers based on error-rate metrics
- Multi-cluster GitOps at organizational scale
- Designing a deployment strategy for a zero-downtime, high-traffic service
- Progressive delivery combining canary + automated analysis
Real-World Industry Use Case
GitOps with ArgoCD is now a standard part of advanced production Kubernetes workflows — instead of an engineer manually running deploy commands, a merged pull request automatically becomes the new running state of the cluster.
Monitoring, Logging & Observability
You cannot fix what you cannot see — observability is how a team actually knows a system is healthy, and finds out fast when it isn't.
Basics
- The three pillars: metrics, logs & traces
- Prometheus — collecting metrics from running services
- Grafana — building dashboards from that data
- Basic alerting on a critical metric (error rate, latency)
Intermediate
- Centralized logging — the ELK stack (Elasticsearch, Logstash, Kibana) or Loki
- Distributed tracing across microservices
- Setting meaningful alert thresholds (avoiding alert fatigue)
- On-call basics & incident response workflow
Advanced
- AIOps — anomaly detection tools like AWS DevOps Guru & Azure Monitor AI
- SLOs, SLIs & error budgets as a formal reliability practice
- Correlating metrics, logs & traces during a live incident
- Building an observability strategy for a new service from day one
Real-World Industry Use Case
Prometheus and Grafana are the de facto standard open-source monitoring stack across the industry, while cloud-native AIOps tools like AWS DevOps Guru and Azure Monitor AI are increasingly used to catch anomalies before a human would even notice them.
DevOps Culture, Security & SRE Practice
DevOps was never just tooling — it's a set of practices for how development and operations work together, with security and reliability built in from the start rather than bolted on.
Basics
- What "DevOps culture" actually means — breaking down dev/ops silos
- Shift-left — catching problems earlier in the pipeline, not after deploy
- Basic security scanning integrated into CI/CD (DevSecOps)
- Writing a clear incident postmortem
Intermediate
- Site Reliability Engineering (SRE) as a discipline
- On-call rotations & escalation policies
- Chaos engineering — deliberately testing system resilience
- Blameless postmortems & continuous improvement culture
Advanced
- Designing an organization-wide reliability practice
- Balancing feature velocity against reliability (error budgets in practice)
- Security compliance automation across the DevOps pipeline
- Preparing for industry certifications — AWS/Azure/GCP DevOps Engineer paths
Real-World Industry Use Case
AWS Certified DevOps Engineer, Azure DevOps Engineer Expert, and Google Cloud Professional Cloud DevOps Engineer are consistently named among the leading certifications in the field — proof that the culture and practice side of DevOps is treated as seriously by the industry as the tooling itself.
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