gcp-expert

To design a production-ready GKE microservices architecture on Google Cloud, use Terraform for regional and multi-zone cluster provisioning, configure Workload Identity, and implement centralized Cloud Monitoring and Logging for reliable operations at scale.

Choose GKE for complex microservices needing custom cluster configuration and multi-zone control, whereas Cloud Run suits simpler containerized deployments. Google Cloud architecture selection depends on your specific traffic scale, orchestration, and operational requirements.

Provision GCP infrastructure with Terraform by defining regional and multi-zone resources, enforcing least-privilege IAM, and configuring VPC Service Controls. This ensures secure, repeatable Google Cloud deployments with proper network isolation and cost controls.

Yes, you can use BigQuery for streaming analytics with Dataflow on Google Cloud. Design appropriate BigQuery schemas to ingest streaming data, enabling real-time data processing and analytics within your cloud-native architecture.

Train and deploy machine learning models using Vertex AI by setting up managed training pipelines and endpoint configurations. This Google Cloud service integrates with your existing infrastructure to streamline ML deployment workflows and model serving.

You need VPC Service Controls to secure your Google Cloud architecture if you require strict network boundaries around managed services like BigQuery and Cloud Storage. It prevents data exfiltration and enforces perimeter security alongside IAM best practices.