Description
Chaos Engineering with Python
Chaos Engineering with Python — a practical, hands-on course that teaches you how to design, implement, and run chaos experiments using Python to improve system resilience, reliability, and observability. Use this introduction as a concise meta description for SEO: “Chaos Engineering with Python — Learn to design and execute fault-injection experiments, automate chaos workflows with Python, and build observable, failure-resistant systems.”
Course Overview
This course takes a pragmatic approach to chaos engineering. You will move from foundational theory (why we intentionally break systems) to practical Python-based toolchains for building repeatable experiments. Through guided labs and real-world scenarios, you’ll learn how to craft hypotheses, run safe blast-radius experiments, and measure impact using telemetry and observability data.
Who Should Enroll
- Site Reliability Engineers (SREs) and DevOps practitioners who want to proactively improve uptime.
- Backend and platform engineers responsible for distributed systems, microservices, or cloud infrastructure.
- QA and test engineers seeking to extend testing into production-like failure modes.
- Python developers interested in automation, chaos tool integration, and observability pipelines.
What You’ll Learn
- Core principles and mindset of chaos engineering: hypothesis-driven experiments and safe blast radius.
- How to design failure experiments that reveal systemic weaknesses instead of surface bugs.
- Using Python to script chaos experiments, automations, and experiment orchestration.
- Integrating chaos tests with observability stacks (metrics, logs, traces) to measure impact.
- Implementing rollback and remediation strategies and automating safety guards.
- Building CI pipelines that incorporate chaos as part of continuous verification.
Course Modules (Detailed)
- Introduction & Theory — Definitions, historical context, the difference between testing and chaos engineering, safety culture, and ethics.
- Designing Experiments — Forming hypotheses, choosing metrics, defining blast radius, and failure modes.
- Python Tooling for Chaos — Using Python to create repeatable experiments, examples with Chaos Toolkit and custom Python scripts.
- Service-Level Observability — Instrumentation, metric selection (SLOs/SLIs), and using telemetry to validate experiments.
- Infrastructure & Cloud Scenarios — Network faults, CPU/memory faults, container orchestration, and cloud-specific failure cases.
- Automating & Scheduling Experiments — CI integration, safe rollouts, and experiment orchestration patterns.
- Post-Experiment Analysis — Root-cause analysis, runbooks, and turning findings into engineering improvements.
- Capstone Project — Plan and execute a full chaos experiment on a sample microservice architecture and present findings.
Hands-on Labs & Projects
Every module includes practical labs. You’ll write Python scripts to inject faults, use the Chaos Toolkit or a mocked Gremlin-style API, collect metrics from Prometheus (or simulated telemetry), and prepare remediation runbooks. The capstone ties everything together with a guided failure injection exercise on a staged microservice stack.
Prerequisites
Familiarity with Python basics (functions, modules, virtualenv), basic Linux command-line skills, and a foundational understanding of distributed systems (HTTP, containers, container orchestration like Kubernetes recommended but not strictly required).
Course Format & Duration
Format: Video lessons, downloadable code notebooks, step-by-step lab guides, and assessment quizzes. Duration: ~20–30 hours of paced content plus the capstone project (self-paced).
Instructor & Credentials
Delivered by industry-experienced SREs and Python developers with real-world experience running production chaos programs. Each module includes sample code, suggested reading, and pointers to production-grade tooling.
Outcomes & Career Impact
Graduates will be able to design and run safe chaos experiments, integrate chaos into CI/CD pipelines, and use Python to automate resiliency checks — improving system reliability and making data-driven reliability investments. This course strengthens SRE, DevOps, and platform engineering profiles.
FAQs
- Do I need cloud access?
- Basic labs run locally (Docker/Kubernetes kind clusters). Cloud examples are provided; optional cloud access enhances learning.
- Will I get code samples?
- Yes — full Python scripts, example CI pipelines, and observability dashboards are included.
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