CourseGenix

DevOps with GitHub Actions

6 Units26 Lessons

Unit 1

Introduction to GitHub Actions Workflows

Understanding GitHub Actions Workflows Basics

Real-World Project: Creating a Simple Workflow for Repository Events

Defining Events and Triggers in Workflows

Unit 2

Basic CICD

Configuring Simple CD Workflows

Running Builds and Deployments Basics

Monitoring Basic CI/CD Pipelines

Setting Up a Basic CI Pipeline

Real-World Project: Automating a Simple Application Deployment

Unit 3

Managing Secrets and Environments in CI/CD

Best Practices for Secret Management

Real-World Project: Securing a CI/CD Pipeline with Environments

Storing and Using Secrets in GitHub Actions

Configuring Environments for CI/CD Workflows

Unit 4

Automating Testing and Code Quality Checks

Integrating Automated Testing into Workflows

Setting Up Code Quality Checks

Using Linters and Static Analysis Tools

Running Unit and Integration Tests

Real-World Project: Automating a Full Testing Suite for an Application

Unit 5

Advanced Deployment Strategies with GitHub Actions

Implementing Blue-Green Deployment Strategies

Exploring Canary and Rolling Deployments

Optimizing Deployment Workflows for Scalability

Real-World Project: Deploying an Application with Advanced Strategies

Unit 6

Integrating GitHub Actions with Other DevOps Tools

Connecting GitHub Actions with Container Orchestration Tools

Linking with Monitoring and Logging Services

Integrating with Cloud Provider APIs

Real-World Project: Building an Integrated DevOps Pipeline

Handling API and Webhook Integrations

Unit 5•Chapter 3

Optimizing Deployment Workflows for Scalability

Summary

Instructions specify summaries must be 250 words or less, exclude sponsors and unrelated content, and avoid any introductory statements about the topic.

Concept Check

0/5

What is the primary advantage of using auto-scaling in deployment workflows?

Automatically adjusts resources based on demand

Increases costs by overprovisioning resources

Reduces deployment time significantly

Eliminates the need for monitoring tools

In Kubernetes, which tool ensures horizontal scaling of pods?

Horizontal Pod Autoscaler manages scaling

Deployment Controller updates configurations

Vertical Pod Autoscaler adjusts CPU

Cluster Autoscaler resizes nodes

How does blue-green deployment optimize scalability?

Increases resource usage unnecessarily

Reduces testing requirements entirely

Limits application to single environment

Enables zero-downtime updates for scaling

What role does canary releasing play in workflows?

Ignores performance metrics completely

Deploys all changes at once

Only works for small-scale applications

Gradually rolls out updates to test scalability

Why is monitoring essential for optimizing deployments?

Focuses solely on security aspects

Increases manual intervention required

Replaces the need for CI/CD pipelines

Detects bottlenecks to scale resources efficiently

PreviousExploring Canary and Rolling Deployments

NextReal-World Project: Deploying an Application with Advanced Strategies