Beginner's DevOps Course

Instructor: CEO and co-founder of LayerCI

Course Objective

DevOps: Methodology for building better products by continuously integrating user feedback.
Shift from traditional factory-style software development to continuous updates via the internet (e.g., Facebook).
Formalized into a cycle of planning, coding, building, testing, releasing, operating, and feedback.

Pull Request Automation: Speeds up developer feedback cycles.
- Atomic changes (pull requests) formed, reviewed, and merged quickly.
- Tools: CI systems, ephemeral environments, automated testing, and code review enhancements.
Deployment Automation: Executes code deployment seamlessly (e.g., via Canary deployments).
Application Performance Management: Ensures product health, optimizes resources, and addresses issues (e.g., logging errors, monitoring performance).

Test-Driven Development (TDD): Writing tests before code to satisfy specifications.
- Types of tests: Unit tests, integration tests, system tests, acceptance tests.
Continuous Integration (CI): Developers merge small changes regularly; tests run automatically.
- Benefits: Improved speed, reduced downtime, scalability.
- Common tools: GitHub actions, GitLab pipelines, LayerCI.
Continuous Deployment: Automates deployment after tests pass, facilitating regular updates.

Rolling Deployments: Gradually replace old versions with new ones to minimize downtime.
Blue-Green Deployments: Maintain two environments (e.g., blue and green) and switch traffic between them for seamless updates.
Canary Deployments: Introduce changes to a small subset of users before a full rollout.

Auto Scaling: Dynamically adjust resources based on demand (e.g., AWS EC2 spot instances).
Serverless: Quickly start and stop computing resources in response to events.

Reverse Proxies: Direct traffic to the appropriate services (e.g., Nginx).
DNS-Based Discovery: Use DNS to dynamically discover and bind services, simplifying updates and scaling.

Log Aggregation: Centralizes logs for easier monitoring and diagnosis (e.g., ELK stack: Elasticsearch, Logstash, Kibana).
Metric Aggregation: Collects and analyzes quantitative data on system health (e.g., CPU usage, request counts, server resources).
- Key tools: Prometheus, Grafana, Datadog, New Relic.
Alerting: Automatically notifies teams of issues, minimizing downtime and enhancing response times.

DevOps engineering is crucial for reliable and efficient software development.
Early automation and effective resource management lead to better product stability and scalability.
As products mature, investing in advanced DevOps practices becomes more essential.

Examples of setting up CI pipelines using tools like LayerCI.
CI examples: Running automated tests, deploying ephemeral environments for real-time feedback.
CD examples: Automatic production updates from changes in the main branch.
Importance of integrating CI/CD for rapid, reliable deployment cycles.

Linting: Automated style checks to maintain code quality (e.g., ESLint, PyLint, Go Format).
Ephemeral Environments: Temporary, self-contained environments for each feature branch, enabling isolated and explicit change reviews.

Hands-on example using LayerCI to set up CI/CD pipelines.
Deploying real changes and reviewing updates in live environments for rapid feedback.

DevOps integrates development and operations for continuous improvement and deployment.
Core pillars (pull request automation, deployment automation, application performance management) are essential for modern development practices.
Deployment strategies and scaling methods play crucial roles in maintaining product stability and performance.