About Cloud Engineering
Cloud engineering is the practice of designing, building, and running the
infrastructure that modern software depends on: servers, storage, networks, and
databases delivered on demand by providers like AWS,
Microsoft Azure, and Google Cloud. Before touching
any provider, it helps to understand Cloud Fundamentals, then the
foundations every cloud engineer leans on: Linux,
Networking, and Scripting with Bash and Python to
automate repetitive work.
Version Control with Git, and hosting a repo on a platform like
GitHub or GitLab, keeps code and infrastructure changes tracked and shareable across a
team. From there you compare the three major providers side by side in a single module. Each names things
differently, but compute, storage, databases, networking, and identity work the same
way underneath, so what you learn on one transfers to the others. Then come
Storage & Databases, Cloud Networking, and
Identity & Security to control who and what can reach each
resource.
Modern cloud work is automated. Infrastructure as Code with tools
like Terraform makes environments repeatable, Containers with Docker
package applications consistently, Orchestration with Kubernetes runs
them at scale (through EKS, AKS, or GKE), and CI/CD pipelines ship
changes safely and often.
Around that core sit the practices that keep a platform healthy:
Monitoring & Observability, so you know what your systems are
doing before users tell you, and Cost & Reliability, keeping
bills predictable and services resilient when something inevitably fails.
It's a role with many entry points: engineers arrive from IT support, system
administration, and software development, and the skills carry across startups,
enterprises, and consultancies. This course walks through that whole stack in order,
so each module builds directly on the last.
Content
15 modules
01
Cloud Fundamentals
What the cloud is and why it matters: service models, shared responsibility, regions, availability zones, pricing, and how AWS, Azure, and Google Cloud compare.
IaaS / PaaS / SaaS
Shared Responsibility
Regions & AZs
Multi-Cloud
02
Linux
The command line, file system, permissions, processes, and services: the operating system most cloud workloads run on.
Linux
Bash
Permissions
systemd
03
Networking
IP addressing, subnets and CIDR, DNS, HTTP/HTTPS, TCP/UDP, firewalls, and load balancing concepts.
TCP/IP
DNS
CIDR
Firewalls
04
Scripting
Automating everyday tasks with Bash and Python, including working with JSON, REST APIs, and cloud SDKs.
Bash
Python
JSON
APIs
05
Version Control
Git fundamentals (branching, commits, and pull requests) plus hosting a repo on GitHub or GitLab to collaborate on code and infrastructure.
Git
VCS Hosting
Collaboration
06
Cloud Providers (AWS, Azure, Google Cloud)
The three major clouds side by side: core compute, storage, and database services, the console and CLI of each, service-name equivalents, and the entry certifications (AWS Cloud Practitioner, AZ-900, Cloud Digital Leader).
AWS
Azure
Google Cloud
Service Mapping
07
Storage & Databases
Choosing the right data service: object, block, and file storage, relational and NoSQL managed databases, backups, and replication.
Object Storage
RDS
DynamoDB
Cosmos DB
Firestore
08
Cloud Networking
Designing private networks in the cloud: VPCs and VNets, subnets, routing, security groups, load balancers, DNS, and CDNs.
VPC
VNet
Load Balancers
CDN
09
Identity & Security
Controlling access and protecting data: IAM users, roles and policies across providers, least privilege, encryption, and secrets management.
IAM
Entra ID
Least Privilege
Secrets
10
Infrastructure as Code
Defining and versioning infrastructure with code: Terraform across providers, plus CloudFormation, Bicep, state, and modules.
Terraform
CloudFormation
Bicep
Modules
11
Containers
Packaging applications with Docker: images, Dockerfiles, registries, networking, and running containers on managed cloud services.
Docker
Images
Registries
ECS / ACI / Cloud Run
12
Orchestration (Kubernetes)
Running containers at scale: pods, deployments, services, scaling, and managed clusters on EKS, AKS, and GKE.
Kubernetes
EKS
AKS
GKE
13
CI/CD
Automating build, test, and deploy with pipelines so that changes ship safely and repeatedly.
CI/CD
GitHub Actions
Azure DevOps
Pipelines
14
Monitoring & Observability
Knowing what your systems are doing: metrics, logs, traces, dashboards, and alerting that wakes you up for the right reasons.
Metrics
Logs
CloudWatch
Azure Monitor
15
Cost & Reliability
Keeping cloud systems affordable and resilient: cost optimization, backups, disaster recovery, and the Well-Architected principles.
FinOps
Backups
Disaster Recovery
High Availability
Projects
5 builds
The Cloud Resume Challenge: AWS
Host your resume as a static site on S3 and CloudFront with a custom domain and HTTPS, add a serverless visitor counter with API Gateway, Lambda, and DynamoDB, then automate it all with Infrastructure as Code and CI/CD.
The Cloud Resume Challenge: Azure
Build a resume site on Azure Storage with a visitor counter powered by Azure Functions and Cosmos DB, deployed automatically with GitHub Actions.
Cloud Resume API: Google Cloud
Build and deploy a serverless API on Google Cloud with Cloud Functions and Firestore that serves resume data as JSON, integrated with GitHub Actions for automatic deployment.
Deploy an App with Kubernetes and Amazon EKS
A step-by-step project demo: create an EKS cluster, deploy a containerized application to it, and expose it to the internet using Kubernetes.
Automate AWS Infrastructure with Terraform
Learn Terraform on AWS from first steps to a modular, automated setup driven by GitHub, so the whole environment can be created and destroyed from code.
What You Can Do After This Course
By the end of the curriculum you'll have shipped five real projects and covered the
full stack a professional cloud engineering role expects. Concretely, you'll be able to:
Work confidently in Linux: use the command line, manage services and permissions, and script routine tasks in Bash and Python.
Work across AWS, Azure, and Google Cloud: deploy compute, storage, and managed databases on each, and translate skills from one provider to another.
Design cloud networks: build VPCs and VNets with sensible subnets, routing, load balancers, and security groups.
Secure by default: apply least-privilege access, encryption, and secrets management from the start.
Automate everything: define infrastructure with Terraform and ship changes through CI/CD pipelines.
Run containers at scale: package apps with Docker and deploy them on Kubernetes using EKS, AKS, or GKE.
Operate and optimize: monitor systems, control costs, and design for reliability and recovery.
Show proof of skill: three cloud-hosted resumes, a Kubernetes deployment, and a Terraform-built network, plus a certificate.