27 docs tagged with "sre"

A 12-week phased learning plan from Linux comfort to production-grade SRE debugging skill — filesystem, processes, networking, CPU/memory, containers, and observability.

SRE-focused advanced Python topics: infrastructure automation, monitoring and observability, incident response, chaos testing, and security.

End-to-end reference for AI-Assisted Engineering Workflows — where coding assistants fit in SRE work, prompt engineering basics, guardrails for production changes, and interview-ready Q&A.

End-to-end reference for AWS — compute, storage, networking, IAM, databases, high availability, and interview-ready Q&A.

End-to-end reference for chaos engineering — the steady-state hypothesis, the experiment lifecycle, failure injection types, tools, Game Days, blast-radius safety, and interview-ready Q&A.

End-to-end reference for CI/CD — pipeline concepts, deployment strategies, side-by-side Jenkins/GitLab CI/GitHub Actions syntax, and interview-ready Q&A.

Provider-agnostic architecture patterns — HA design, network topology, compute choice, load balancing, autoscaling, and DR — complementing the AWS-specific and Terraform-specific guides elsewhere in this site.

Milestone-by-milestone Python syllabus for SRE, SDET, and SDE — core foundations first, then role-specific tracks.

Rapid-fire practice material for SRE and DevOps troubleshooting interviews — Linux, AWS+Linux combined incidents, Kubernetes, and a 15-pattern incident catalog.

Deep filesystem, storage, and disk-incident debugging knowledge for production SRE work — FHS, inodes, LVM, RAID, permissions, and a full storage incident debugging playbook.

Org-level incident management scaffolding — severity classification, IC/Comms/Scribe roles, on-call and escalation policy, PagerDuty/Opsgenie alert routing, communication templates, blameless postmortems, and runbook design.

Navigation map for the Incident Response Mastery series — org-level incident management setup, mindset, hands-on simulations, a 90-day roadmap, and deep Linux/kernel/interview references.

The internal thought process experienced SREs execute automatically under pressure — USE method, blast radius, mitigation before root cause, and the 5 Whys.

10 timed production incident simulations plus 6 hands-on labs — CPU saturation, disk-full, OOM kills, SYN backlog exhaustion, cgroup throttling, DNS breakage, and more.

End-to-end reference for Kubernetes — architecture, core objects, networking, scheduling, health checks, rollouts, and interview-ready Q&A.

End-to-end reference for Linux Administration — filesystem, permissions, process/service management, networking, and interview-ready Q&A.

Master outline of Linux subsystems and tools an SRE must know cold — systemd, CPU/memory/disk, networking, observability, security, containers, boot, and the practical debugging playbook.

Conceptual foundation of how the Linux kernel manages processes, memory, I/O, and hardware — scheduling, user/kernel space, syscalls, interrupts, context switching, and modules.

End-to-end reference for Networking Fundamentals — OSI/TCP-IP model, addressing, DNS/TLS, load balancing, troubleshooting tools, and interview-ready Q&A.

End-to-end reference for Observability (Grafana & Prometheus) — Prometheus architecture and PromQL, SLIs/SLOs and error budgets, alerting and dashboards, long-term storage, ELK/EFK and commercial APM tradeoffs, and interview-ready Q&A.

End-to-end reference for OpenTelemetry — traces, metrics, logs, SDK/Collector architecture, instrumentation, sampling, and interview-ready Q&A.

Command-level and conceptual mastery of Linux process debugging via CLI and /proc — lifecycle, ps/top/kill, decision trees, deep scenarios, and interview Q&A.

The full Python learning path in one document — objectives, topics with code, mini-projects, role tracks, and troubleshooting.

How Python fundamentals map to reliability work — safe CLIs, log parsing, health checks, metrics, SLOs, Kubernetes automation, runbooks, and chaos.

Which Python skills matter most for SDET, SDE, and SRE roles, and how to use the role guides after learning the core language.

End-to-end reference for diagnosing Linux system performance with the USE and RED methods — CPU, memory, disk I/O, network, the standard toolkit, and interview-ready Q&A.

End-to-end reference for Terraform — IaC philosophy, core workflow, HCL syntax, state management, modules, and interview-ready Q&A.