DevOps & Infrastructure

The foundation everything else builds on. You'll learn to navigate a Linux system, create and manage files, control who can access what, and chain commands together to automate tasks. By the end, you'll be able to set up users, connect to servers over SSH, install software, and run your first Docker container.

Running a Linux server means keeping it healthy, secure, and automated. Job scheduling, log management, firewalls, disk management, backups, shell environments, systemd services, and text processing with awk and sed.

DevOps foundations: run multi-container apps with Docker Compose (health checks, secrets, networks), configure Nginx as a reverse proxy with SSL, generate and inspect TLS certificates, use Git branches/rebase/tags, set up GitHub Actions CI/CD pipelines, write robust shell scripts with functions and error handling, monitor services with curl and systemd, and troubleshoot the network stack with dig, nc, curl, and tcpdump.

Power tools for serious Linux work. vim for efficient editing on any server. Systemd unit authoring for custom services. iptables for real firewall control. lsof and strace for debugging. Netcat for network testing. Ansible for automation. Make for task running.

Infrastructure as Code from first principles. You'll understand why declarative beats imperative, use Terraform to manage AWS resources (init/plan/apply/state/output), compose infrastructure with modules and tfvars, query the cloud with aws ec2/s3/iam via the CLI and jq, audit S3 lifecycle and bucket policies, read IAM policies for least-privilege violations, build GitHub Actions pipelines that plan on PRs and apply only from main, and analyze three-tier architectures with DR runbooks covering RTO/RPO tradeoffs.

Kubernetes operations from the kubectl command line. You'll understand the control plane vs nodes, create and manage pods and deployments, expose workloads via Services, inject configuration with ConfigMaps and Secrets, isolate workloads with namespaces and resource quotas, diagnose common failures (CrashLoopBackOff, Pending, ImagePullBackOff) with kubectl logs/describe, and run the full deploy-scale-update-rollback lifecycle.

Production observability from first principles. You'll understand metrics (counter/gauge/histogram/summary), configure Prometheus scrape jobs and recording rules, write and debug PromQL (rate, histogram_quantile, aggregation), build SLO-based alert rules that avoid false alarms, analyze structured JSON logs with jq, design dashboards for the RED method (rate/errors/duration), define SLIs and SLOs with error budgets, and run incident investigations correlating metrics, logs, and deploy events.

Platform engineering in practice. You'll operate a real multi-container stack (web + api + db + prometheus + grafana) with docker compose, separate config per environment with .env files and manifest overrides, build deploy and rollback scripts that read versions.log as an audit trail, promote config between dev/staging/prod with GitOps scripts, monitor the live stack via Prometheus query API and Grafana, scale horizontally with docker compose --scale, and run a full incident simulation (deploy broken → detect → diagnose → rollback → verify).

Python for DevOps — the automation language. You'll start from zero Python, learn to parse YAML/JSON configs and call subprocess, work with HTTP APIs (auth, pagination, retry/backoff, rate limits), automate Docker with docker-py, build deployment scripts with manifests + pre-flight + rollback + structured logs, write health-check and monitoring loops with failure thresholds and Prometheus exposition, compose a complete deploy tool from argparse through summary table, and finish with an infra monitor capstone that generates a self-contained HTML status report. Two labs: a deployment pipeline tool with rollback, and a monitoring tool that computes uptime and generates HTML reports.

Four cross-level master labs that synthesize skills from L1-L9. Zero-Downtime Migration exercises scaling + compose overrides + background traffic verification. Chaos Engineering runs 4 controlled experiments (kill, drop, restart, fill) with hypothesis→observation→recovery documentation. Security Hardening Sprint reads a CIS benchmark and proves each mitigation is present in a hardened compose. Capacity Planning analyzes 4 weeks of CSV metrics to project when CPU will run out and writes a data-backed scaling plan. Progress tracked separately — does not count toward the L1-L9 totals.