Your platform engineering team runs all workloads on GKE and applies every change through a GitOps pipeline that simply runs "kubectl apply" on YAML stored in a Git repository. They now need the same pipeline to create and update Google Cloud resources such as Pub/Sub topics, Cloud SQL instances, and IAM service accounts. The solution must let these resources be expressed as YAML objects that can be applied with kubectl, avoiding any separate tooling outside the cluster. Which approach best satisfies these requirements?
Create Deployment Manager templates for each resource type and trigger Deployment Manager updates from the pipeline.
Package shell scripts that run gcloud commands into Helm post-install hooks and apply the chart from the pipeline.
Insert Terraform plan and apply steps into the pipeline and use the Google Cloud provider to manage the required resources.
Install Config Connector in the GKE cluster and describe the desired Pub/Sub, Cloud SQL, and IAM resources as Kubernetes custom resources in the same Git repository.
Config Connector extends Kubernetes with Custom Resource Definitions (CRDs) that map to Google Cloud resources. After the add-on is installed in the cluster, engineers can declare resources such as Pub/SubTopic, IAMServiceAccount, or SQLInstance in ordinary YAML and apply them with kubectl. Because the CRDs are stored in Git and reconciled by the Kubernetes control loop, the existing GitOps workflow continues to function without adding an external provisioning system. Terraform, Helm hooks, or Deployment Manager would introduce additional tools or APIs outside the native kubectl workflow, violating the stated constraint.
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What is Config Connector?
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What are Custom Resource Definitions (CRDs)?
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How does kubectl interact with Config Connector?
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What is Config Connector in GKE?
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How does Config Connector work with GitOps pipelines?
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Why not use Terraform or Helm hooks instead of Config Connector?
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GCP Associate Cloud Engineer
Planning and implementing a cloud solution
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