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A DevOps team is designing an enterprise CI/CD pipeline on Google Cloud for a mission-critical application hosted on Google Kubernetes Engine (GKE). The pipeline architecture must satisfy the following technical and operational requirements:
Which end-to-end pipeline workflow should the team implement?
This pattern attempts to bypass container image registries by pushing source code tarballs to Cloud Storage, passing the source code to Google Cloud Deploy, and expecting target GKE cluster nodes to compile and execute containers locally without Skaffold.
skaffold.yaml configuration to render manifests and apply deployments. It does not compile source code or build container images on target nodes.You were implementing a legacy VM deployment pattern on Compute Engine managed instance groups where instances download application source or zip packages during startup via startup scripts.
This architecture establishes an end-to-end, enterprise-grade Continuous Integration and Continuous Delivery (CI/CD) pipeline using Cloud Build, Artifact Registry, Artifact Analysis, Binary Authorization, and Google Cloud Deploy with Skaffold.
skaffold.yaml to execute skaffold render and skaffold apply to deploy workloads progressively across targets (e.g., staging and production). Following each deployment stage, Cloud Deploy executes automated deployment verification jobs configured in Skaffold to validate service health and minimize downtime.This solution follows Google Cloud's recommended golden path and enterprise blueprint for secure software delivery. It centralizes artifact governance, enforces strict software supply chain security via SLSA frameworks, and eliminates fragile custom deployment scripts.
This option builds container images in Cloud Build, publishes them to Artifact Registry, and executes a Recreate deployment strategy on GKE while relying on operators to manually inspect security vulnerabilities in the Google Cloud console.
The application used a single-instance legacy database or stateful backend that could not support two coexisting application versions accessing the same datastore simultaneously.
This option relies entirely on Cloud Build to handle both CI and CD tasks directly by running imperative kubectl apply commands against target clusters using the Cloud Build service account, followed by asynchronous scheduled polling via Cloud Scheduler.
kubectl inside Cloud Build lacks release orchestration, multi-target promotion management, built-in rollback mechanisms, and integrated stage verification phases. Cloud Scheduler runs external cron jobs asynchronously and cannot act as a synchronized deployment verification gate within the rollout pipeline.The workload was a simple non-production prototype where a lightweight, single-script continuous deployment process was required without compliance, governance, or multi-target promotion requirements.
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