Cloud DevOps Essentials - Part 1

How does the cloud help in managing the DevOps lifecycle?

Author

Vishakha Sadhwani
May 15, 2025

Hi Inner Circle,

If you're starting your cloud journey and preparing for DevOps interviews—you're in the right place.

One of the most common interview themes is understanding the managed services offered by major cloud providers like AWS, GCP, and Azure—and how these services map to key DevOps functionalities.

Welcome to Week 2 – today, we’ll explore Cloud DevOps Essentials today.

Here’s a quick breakdown to guide your preparation:

Cloud Infrastructure as Code (IaC)

Core Concepts

  • Declarative Provisioning
    You describe the desired final state of your infrastructure, and the IaC tool figures out the steps to reach that state.
    Example: In Terraform, you define an AWS EC2 instance in a .tf file with specific properties, and Terraform ensures it exists exactly as declared.

  • Cloud APIs
    IaC tools interact with cloud providers (like AWS, Azure, GCP) through their APIs—using SDKs, CDKs, or REST APIs.
    Tip: Programming knowledge helps when working with SDK-based or CDK-based tools like Pulumi or AWS CDK.
    Example: Using AWS CDK in TypeScript to define a VPC and an ECS cluster.

  • Idempotency
    Executing the same script multiple times results in the same infrastructure state.
    Example: If your script defines a Lambda function named foo, running the script twice won’t create a duplicate—it will recognize it already exists and skip it or update it if needed.

  • State Management
    IaC tools track the current state of infrastructure to determine what needs to change.
    Example: Terraform uses a .tfstate file to compare your desired configuration with the real-world infrastructure and apply only the necessary changes.

  • Versioned Cloud Resources
    Infrastructure code is stored in version control (like Git), allowing collaboration, rollbacks, and change tracking just like application code.
    Example: Roll back to a previous infrastructure version by reverting a Git commit and reapplying your configuration.

  • Terraform (cloud-agnostic)

  • Pulumi (code-first approach using familiar languages)

  • AWS CloudFormation

  • Azure Resource Manager (ARM) / Bicep

Pick one that aligns with your team’s cloud provider and preferred syntax (YAML, JSON, or code).

Benefits

  • Automated cloud setup

  • Consistency across environments

  • Scalable infrastructure

  • Easier disaster recovery

  • Simplified rollbacks

Cloud CI/CD Pipelines

Core Concepts

  • Continuous Integration (CI)
    Automatically build and test code every time changes are pushed to the repository. This ensures early detection of bugs and integration issues.
    Example: Running unit tests and code linting whenever a developer commits code to the main branch.

  • Continuous Delivery/Deployment (CD)
    Automatically deliver or deploy tested code to staging or production environments. This can include approval gates, blue/green deployments, or canary releases.
    Example: After passing tests, code is deployed to a staging environment and then promoted to production.

  • Pipeline as Code
    CI/CD pipelines are defined in version-controlled files using YAML or scripts. This enables versioning, review, and collaboration.
    Example: A .github/workflows/deploy.yml file defines a GitHub Actions pipeline that builds, tests, and deploys an application.

  • GitOps for Cloud-Native Apps
    A Git-based workflow where deployments are driven by Git commits. Kubernetes and containerized apps are updated by syncing infrastructure/state with Git.
    Example: ArgoCD watches a Git repo and automatically syncs Kubernetes manifests to the cluster.

  • Automated Cloud Deployments
    Automate the full software release lifecycle—from code commit to deployment—across cloud services like VMs, containers, serverless, and PaaS.
    Example: Push to main triggers a build and deploys a Docker container to AWS ECS or Azure App Service.

Pipeline Phases

  1. Source – Trigger the pipeline from version control (e.g., GitHub, GitLab, Bitbucket)

  2. Build – Compile code, resolve dependencies, package the application

  3. Test – Run automated tests (unit, integration, security, etc.)

  4. Deploy – Release to cloud environments:

    • VMs (e.g., AWS EC2, GCE, Azure VM)

    • Containers (e.g., AWS ECS/EKS, Azure AKS, GKE)

    • Serverless (e.g., AWS Lambda, Azure Functions, Cloud Functions)

    • PaaS (e.g., Beanstalk, Google App Engine)

  • AWS CodePipeline (integrated with CodeBuild, CodeDeploy, etc.)

  • Azure DevOps Pipelines (powerful YAML-based or visual pipelines)

  • Google Cloud Build (native GCP CI/CD)

  • Jenkins (open-source, highly customizable)

  • GitLab CI/CD (integrated with GitLab repos)

  • GitHub Actions (native GitHub CI/CD with marketplace actions)

Choose a tool based on your cloud provider, team expertise, and integration needs.

Benefits

  • Faster release cycles

  • Improved code quality through automation

  • Consistent and repeatable deployments

  • Easier rollback and monitoring

  • Enhanced developer productivity

Cloud Container Orchestration

Core Concepts

  • Managed Kubernetes (K8s)
    Cloud providers offer fully managed Kubernetes services to simplify deployment, scaling, and management of containerized applications.
    Example: Amazon EKS handles the control plane while you manage your workloads and configurations.

  • Serverless Containers
    Run containers without managing servers. Ideal for event-driven apps or sporadic workloads.
    Examples: AWS Fargate, Google Cloud Run, Azure Container Apps. No need to provision or manage VMs or clusters.

  • Pod and Service Autoscaling
    Automatically adjust compute resources based on traffic or usage metrics.
    Example: Horizontal Pod Autoscaler in Kubernetes increases the number of pods when CPU usage exceeds a defined threshold.

  • Amazon EKS / ECS – Elastic Kubernetes Service or Elastic Container Service

  • Azure AKS – Azure Kubernetes Service

  • Google GKE – Google Kubernetes Engine

  • All offer managed control planes, integration with their ecosystems, and autoscaling support.

Container Technologies

  • Docker – Standard for containerizing applications

  • Container Registries:

    • Amazon ECR (Elastic Container Registry)

    • Azure ACR (Azure Container Registry)

    • Google GCR / Artifact Registry

These registries store container images and integrate directly with orchestration platforms.

Key Tools

  • Helm – Kubernetes package manager for templating and deploying complex applications
    Example: Deploy PostgreSQL with a single command using a Helm chart.

  • Kustomize – Customize Kubernetes YAMLs without templates
    Example: Reuse a base deployment YAML and overlay environment-specific configs.

  • Service Meshes – Provide observability, traffic control, and security between microservices

    • Examples: AWS App Mesh, Istio, Linkerd
      Use case: Secure and monitor communication between services without changing app code.

Benefits

  • Scalable and resilient infrastructure for microservices

  • Efficient resource utilization via autoscaling

  • Simplified operations through managed services

  • Consistent environments across dev, staging, and prod

  • Support for hybrid and multi-cloud deployments

Cloud Release Strategies

Core Concepts

  • Blue/Green Deployments
    Two identical environments ("blue" = current, "green" = new) are maintained. Traffic is switched from blue to green once the new version is verified.
    Example: Deploy a new version to the green environment, run tests, and then update the load balancer to route traffic to green.

  • Canary Releases
    Gradually release a new version to a small subset of users, monitor for issues, then incrementally increase traffic.
    Example: Route 5% of traffic to the new version using AWS Application Load Balancer or Google Cloud Traffic Director.

  • Rolling Updates
    Update instances in batches, replacing the old version with the new one while maintaining service availability.
    Example: Kubernetes rolling updates replace pods one at a time using the deployment controller.

Cloud Enablers

  • Cloud Load Balancers – Distribute traffic between old and new versions (e.g., AWS ALB/NLB, Azure Load Balancer, GCP Load Balancer)

  • Auto Scaling Groups – Support rolling updates and staged deployment across fleets of instances

  • Deployment Slots – Used in Azure App Service for staging environments and easy traffic swaps
    Example: Deploy to the "staging" slot, test, then swap with production instantly.

  • Traffic Splitting – Control what percentage of traffic is routed to each version
    Example: Google Cloud Run supports traffic splitting via configuration settings.

  • AWS CodeDeploy – Supports blue/green and canary strategies natively

  • Azure DevOps Releases – Integrated pipelines with deployment slots and approval gates

  • Spinnaker – Multi-cloud continuous delivery tool with built-in support for deployment strategies

  • Feature Flag Platforms – Toggle features for users without redeploying code

    • LaunchDarkly, CloudBees Feature Management, Flagsmith

Benefits

  • Minimize downtime and risk during releases

  • Enable rapid rollback in case of failure

  • Improve user experience with phased rollouts

  • A/B testing and experimentation enabled through traffic management

  • Safer cloud-native deployments using built-in platform tools

Each of these topics is essential for understanding how modern cloud-native delivery works—or at the very least, the foundational DevOps concepts you should be familiar with as you grow in your cloud engineering role.

Now, you’re not expected to build end-to-end enterprise CI/CD platforms or automate complex infrastructure just yet.

But this is where it all starts.

Projects:

Use these resources to start:

Free Resources:

Check out the github repo links here.

That’s it for today! Next week, we’ll dive into Part 2 of your Cloud DevOps journey.

See you next Thursday!

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