15-week GCP DevOps Bootcamp for beginners

Demo Classes

Bootcamp Details

Most bootcamps teach you tools. We teach you to build production systems that actually work. In 15 weeks, you’ll go from basic Linux knowledge to deploying secure, scalable applications on Google Cloud that companies pay DevOps engineers six figures to build.

About This Bootcamp

This is a hands-on, project-driven bootcamp designed to give you real-world DevOps experience on Google Cloud Platform. You’ll learn by doing, building production-grade applications from scratch, automating deployments, and following industry best practices. By the end, you’ll have a portfolio of projects that prove you can handle real DevOps challenges.

Prerequisites

Basic Linux command line knowledge
Understanding of how web applications work
A Google Cloud account (free tier works fine)

Topics and Tools Covered

Cloud Platform: Google Cloud Platform (GCP), Cloud Run, GKE, Cloud Functions, Cloud SQL, Cloud Storage, Artifact Registry

Containerization: Docker, Docker Compose, Container Registry

Infrastructure as Code: Terraform, Terraform Modules

CI/CD: Cloud Build, GitHub Actions, ArgoCD

Version Control: Git, GitHub

Orchestration: Kubernetes, GKE, Helm

Monitoring & Logging: Cloud Monitoring, Cloud Logging, Prometheus, Grafana

Security: IAM, Secret Manager, DevSecOps practices, SonarQube, Trivy

Programming: Python for DevOps automation

Serverless: Cloud Functions, Pub/Sub, Cloud Scheduler

GitOps: ArgoCD, Kubernetes manifests

Week 1: Introduction to Cloud DevOps and GCP

Class 1: Introduction to Cloud, DevOps, and SDLC

Understanding SDLC and its evolution: Waterfall → Agile → DevOps
What is DevOps and what problems does it solve?
DevOps tools landscape and where each tool fits
Overview of GCP’s most used services in modern tech stacks
Introduction to GCP networking, compute, and storage fundamentals

Class 2: Getting Started with GCP

Setting up your first GCP project
Installing and configuring gcloud SDK
Navigating the GCP Console
Running commands in Cloud Shell
Lab: Deploy basic resources from GCP Console
Lab: Host a static website using Cloud Storage

Week 2: Containers, Docker, and Multi-Container Applications

Class 3: Introduction to Docker and Container Management

What is Docker and how does it work?
Running Docker containers locally
Docker networking and volumes
Building custom Docker images
Storing images in public and private repositories
Introduction to Artifact Registry

Class 4: Multi-Container Applications with Docker Compose

What is Docker Compose and why use it?
Building a multi-container application
Project: Deploy an app with monitoring, alerting, and load testing
Using SMTP for email alerts
Managing container dependencies and networking

Week 3: Running Containers in Production with Cloud Run

Class 5: Introduction to Cloud Run

Running your first app on Cloud Run
Cloud Run Jobs vs Services with real-world examples
Dockerizing a custom application for Cloud Run deployment
Lab: Deploy a containerized app to Cloud Run

Class 6: Production-Ready Cloud Run

Scaling strategies for Cloud Run applications
Setting up custom domains and SSL certificates
Configuring load balancers
Implementing security best practices
Lab: Deploy a production-ready app with custom domain

Week 4: Git, GitHub, and CI/CD Fundamentals

Class 7: Version Control and Cloud Build

Git fundamentals with real-world use cases
GitHub workflows for team collaboration
Introduction to CI/CD with GCP Cloud Build
Integrating Cloud Build with Cloud Run
Lab: Set up a basic Cloud Build pipeline

Class 8: Automating Deployments with GitHub Actions

Automating builds and deployments for Cloud Run apps
Introduction to GitHub Actions for cloud-agnostic CI/CD
Building reusable workflows
Project: Deploy the Week 3 app using GitHub Actions
Comparing Cloud Build vs GitHub Actions

Week 5: Infrastructure as Code with Terraform

Class 9: Terraform Fundamentals

What is Infrastructure as Code (IaC) and why it matters
Terraform basics: Providers, resources, and state management
Managing GCP resources with Terraform
Lab: Provision Cloud Storage and Cloud Run with Terraform

Class 10: Advanced Terraform Practices

Terraform best practices for production environments
Creating and using Terraform modules (public and private)
Terraform import for existing resources
Detecting and managing configuration drift
Lab: Build reusable modules for your infrastructure

Week 6: Security, IAM, Monitoring, and Logging

Class 11: GCP IAM and Security Best Practices

Understanding GCP IAM roles and permissions
Principle of least privilege
Real-world secrets management with Secret Manager
Lab: Store API keys in Secret Manager
Lab: Enable authentication for Cloud Run services

Class 12: Monitoring and Logging in Production

Cloud Monitoring and Cloud Logging fundamentals
Creating metrics and alerts for Cloud Run
Debugging with logs and traces
Scaling and performance tuning strategies
Lab: Set up custom metrics for HTTP requests
Lab: Create alerts for high error rates
Mini-Project: Build a comprehensive monitoring dashboard

Week 7: Integration Project – Production App on Cloud Run

Class 13 & 14: Building a Production-Ready Application

DevOps workflow review and best practices
Cost optimization strategies in GCP
Capstone Project: Build and deploy a Todo List web app
- Containerize Flask/Express app with frontend and backend
- Store images in Artifact Registry
- Set up CI/CD pipeline with Cloud Build
- Provision resources with Terraform
- Use Secret Manager for sensitive data
- Implement monitoring with Cloud Monitoring
- Secure with IAM roles
- Deploy to Cloud Run with public URL

Week 8: Python for DevOps Automation

Class 15: Python Fundamentals for DevOps

Essential Python data structures and manipulation
Making API calls with the requests library
Running system commands with os and subprocess
Reading and writing files programmatically
Lab: Build useful DevOps automation scripts

Class 16: Building Real-World Python Projects

Introduction to Cloud Functions and serverless automation
Building a file scanning system for GCS buckets
Error handling and logging best practices
Project: Deploy a Python automation tool on Cloud Functions

Week 9: Serverless Automation with Cloud Functions

Class 17 & 18: Advanced Cloud Functions

Serverless computing concepts and use cases
Cloud Functions overview (Gen 1 vs Gen 2)
Function triggers: HTTP, Pub/Sub, Cloud Storage, Cloud Scheduler
Introduction to GCP Pub/Sub service
Error handling and retry strategies with Python
Cloud Functions Automation Project:
- Function 1: Process inventory updates from CSV uploads
- Function 2: Send email notifications for low stock
- Function 3: Generate daily reports (scheduled)
- Function 4: Optimize images for product photos
Labs:
- Create HTTP-triggered Cloud Function
- Deploy Cloud Storage-triggered function
- Create Pub/Sub-triggered function
- Schedule functions with Cloud Scheduler
Mini-Project: Build automated image processing pipeline

Week 10: Introduction to Kubernetes

Class 19 & 20: Kubernetes Fundamentals

Kubernetes architecture and core concepts
Understanding Kubernetes objects: Pods, Services, Deployments, ConfigMaps, Secrets
Running services on local Kubernetes (minikube/kind)
Kubernetes networking basics
Labs:
- Deploy your first application on Kubernetes
- Expose services and manage configurations
- Work with volumes and persistent storage

Week 11: Running Kubernetes on Google Kubernetes Engine (GKE)

Class 21: GKE Fundamentals

GCP networking for GKE
Creating and managing GKE clusters
Deploying a 3-tier application on GKE
Lab: Set up a GKE cluster and deploy a sample application

Class 22: Microservices and Helm

Running microservices architecture on GKE
Introduction to Helm for package management
Creating and using Helm charts
Lab: Deploy microservices using Helm
Lab: Package your application with Helm

Week 12: Monitoring Kubernetes with Prometheus and Grafana

Class 23: Kubernetes Monitoring Setup

Introduction to Prometheus and Grafana
Deploying Prometheus on GKE
Configuring Prometheus to scrape metrics
Setting up Grafana dashboards
Lab: Install Prometheus and Grafana on GKE

Class 24: Advanced Monitoring and Observability

Creating custom Grafana dashboards for Kubernetes
Integrating Cloud Monitoring with GKE
Setting up alerts in Prometheus and Grafana
Monitoring application metrics and cluster health
Lab: Build comprehensive monitoring for your GKE applications
Lab: Configure alerts for critical metrics

Week 13: GitOps with ArgoCD and Infrastructure Automation

Class 25 & 26: GitOps on GKE

Introduction to GitOps principles
Deploying GKE cluster with Terraform
Installing and configuring ArgoCD
Building automated CI/CD pipelines with GitHub Actions and ArgoCD
GKE and Kubernetes best practices
Project: Set up complete GitOps workflow
- Provision GKE with Terraform
- Deploy ArgoCD
- Automate deployments with GitHub Actions
- Implement GitOps workflows

Week 14: DevSecOps and Advanced CI/CD

Class 27 & 28: Security in DevOps Pipelines

Introduction to DevSecOps principles
Multi-environment pipelines (dev, staging, production)
Advanced GitHub Actions workflows
Git branching strategies for teams
Essential DevSecOps tools:
- Code scanning with SonarQube
- Container image scanning with Trivy
- Code linting and formatting
- Vulnerability scanning
Project: Build a secure CI/CD pipeline
- Implement code quality checks
- Add security scanning
- Deploy to multiple environments
- Automated testing and validation

Week 15: Final Capstone Project

Class 29 & 30: E-commerce Inventory Management System

Build and deploy a complete, production-ready cloud-native application using all learned technologies.

Project Requirements:

Application Architecture:

Frontend: React/Vue app (containerized)
Backend API: Python Flask/FastAPI (containerized)
Database: Cloud SQL (PostgreSQL)
File Storage: Cloud Storage
Microservices: At least 3 services deployed on GKE

GKE Deployment:

Deploy microservices to GKE with proper service mesh
Configure Ingress with SSL/TLS
Implement HPA (Horizontal Pod Autoscaler)
Use ConfigMaps and Secrets for configuration
Set up Prometheus and Grafana monitoring

Infrastructure as Code:

Provision all GCP resources with Terraform
Include GKE cluster, Cloud Functions, Cloud Storage, Cloud SQL
Organize code with reusable modules
Manage state properly

CI/CD Pipeline:

GitHub Actions for automated builds and deployments
Separate pipelines for GKE services and Cloud Functions
Automated testing before deployment
GitOps with ArgoCD for Kubernetes deployments
Use Helm for Kubernetes package management

Monitoring & Security:

Prometheus and Grafana dashboards for GKE
Cloud Monitoring dashboards for Cloud Functions
Log-based alerts for errors
IAM roles with least privilege
Secret Manager for sensitive data
Network policies for GKE
DevSecOps tools integrated in pipeline

Documentation:

Architecture diagram
Setup and deployment instructions
API documentation
Monitoring and troubleshooting guide

Deliverables:

GitHub repository with complete source code
Terraform scripts for infrastructure
Kubernetes manifests and Helm charts
CI/CD configuration files
Comprehensive README with setup instructions
Live demo URL for the application
Presentation (10-15 minutes) showcasing the project

Evaluation Criteria:

Functionality and completeness (30%)
Code quality and best practices (20%)
Infrastructure automation (20%)
CI/CD implementation (15%)
Monitoring and security (10%)
Documentation (5%)

Wrap-Up Session

Final Session:

Project presentations and peer feedback
Review of DevOps best practices and lessons learned
Career guidance for DevOps roles
Resume and portfolio building tips
Next steps: GCP certifications
- Associate Cloud Engineer
- Professional Cloud DevOps Engineer
Advanced topics to explore:
- Service Mesh (Istio)
- Anthos for hybrid cloud
- Cloud Composer for workflow orchestration

Bootcamp Outcome

By completing this bootcamp, you’ll have:

Real-world experience running DevOps on GCP with modern tools
A portfolio of production-ready projects
Hands-on experience with Kubernetes, monitoring with Prometheus and Grafana, and Cloud Monitoring
Deep understanding of CI/CD, GitOps, and DevSecOps practices
The skills companies look for in DevOps engineers
Confidence to pursue GCP certifications
A complete capstone project showcasing end-to-end DevOps capabilities