gcp-cloud

Terraform patterns enable infrastructure-as-code deployment across GCP services including Compute Engine, Cloud Functions, Cloud Run, and GKE. Use Terraform modules to codify resource configurations, version control infrastructure changes, and automate provisioning while maintaining consistency across environments.

GCP security best practices include enabling Workload Identity for pod authentication, enforcing least-privilege IAM policies, implementing VPC Service Controls for network isolation, enabling Cloud Audit Logs for compliance tracking, and using Customer-Managed Keys for encryption. These controls harden identity, access, and data protection.

BigQuery cost optimization uses partitioned and clustered table designs to reduce scanned data, applies Committed Use Discounts for predictable workloads, and leverages query optimization techniques. Proper data layout and reservation strategies significantly lower query costs while maintaining performance.

Yes. Terraform patterns support full GKE cluster provisioning with Workload Identity enabled, automating service account binding, IAM role assignment, and OIDC provider configuration. This infrastructure-as-code approach ensures repeatable, auditable cluster deployments with built-in security controls.

This covers Compute Engine, Cloud Functions, Cloud Run, Cloud Storage, Cloud SQL, BigQuery, and GKE. Guidance addresses service selection, integration patterns, security hardening, cost optimization, and governance through Terraform and cloud-native controls for each.

GCP cost reduction combines autoscaling for compute resources, Committed Use Discounts for sustained workloads, and data optimization strategies like BigQuery partitioning. Rightsizing instances and implementing resource quotas further control spend without sacrificing availability or performance.