GCP GKE BigQuery Cloud Run
Google Cloud Platform combines containerised compute on GKE, event-driven serverless on Cloud Run, petabyte-scale analytics on BigQuery and globally distributed databases in Spanner — all governed by a single IAM and org-policy hierarchy. We design, build and operate GCP environments for US and EU product teams, with Terraform-managed infrastructure, VPC Service Controls for data perimeter enforcement, Assured Workloads for EU data residency and CMEK for HIPAA and GDPR-regulated workloads.
Google Cloud Platform combines containerised compute on GKE, event-driven serverless on Cloud Run, petabyte-scale analytics on BigQuery and globally distributed databases in Spanner — all governed by a single IAM and org-policy hierarchy. We design, build and operate GCP environments for US and EU product teams, with Terraform-managed infrastructure, VPC Service Controls for data perimeter enforcement, Assured Workloads for EU data residency and CMEK for HIPAA and GDPR-regulated workloads.
Challenges
GCP IAM spans org, folder, project and resource levels — overly permissive bindings granted at org level propagate silently to every child resource. We audit IAM bindings with Policy Analyzer, enforce least-privilege via org-policy constraints and use Workload Identity for pod-level auth instead of service-account key files.
A flat project structure makes cost allocation, security boundary enforcement and org-policy scoping unmanageable as the organisation grows. We design a folder hierarchy (environments × business units) before provisioning the first resource, with separate projects per environment and Terraform workspaces per folder.
Autopilot removes node management overhead but restricts DaemonSets, host-path volumes and custom node pools — workloads relying on these features break silently at deployment time. We evaluate cluster requirements upfront, choose the appropriate mode and document the ADR so the decision is revisited as requirements evolve.
On-demand BigQuery pricing scales with bytes scanned — unoptimised queries or wide table scans generate surprise bills within hours. We enforce partition pruning via partitioned tables and required partition filters, use materialised views for repeated aggregations and right-size slot commitments versus on-demand for predictable workloads.
Misconfigured VPC-SC perimeters block legitimate API calls (access levels missing, identity mismatches) or leave gaps that allow data exfiltration via shared services. We use dry-run mode to audit denials before enforcement, maintain an access-level inventory in Terraform and test perimeter rules in a staging org before production rollout.
Ensuring EU personal data never crosses to US regions requires coordinated org-policy constraints, correct Assured Workloads folder configuration and per-service residency verification — one misconfigured service can breach the entire residency guarantee. We verify residency at the Terraform plan stage and run automated compliance checks via Security Command Center.
Solutions
Production-grade GKE clusters — Autopilot or Standard — with Workload Identity, Binary Authorization, node auto-provisioning, Istio/Cloud Service Mesh, Argo CD GitOps and cluster autoscaler. Namespace-level resource quotas and network policies enforced from day one.
Event-driven and request-driven services on Cloud Run with VPC connector for private database access, Secret Manager integration, Cloud Build CI/CD pipeline, minimum-instance warm pools for latency-sensitive paths and Cloud Armor edge protection.
Partitioned and clustered dataset design, dbt transformation layer, Dataflow streaming ingestion from Pub/Sub, materialised views, column-level security and Data Catalog tagging for GDPR data classification — all cost-governed with slot commitments and billing alerts.
Cloud SQL PostgreSQL with read replicas, automated backups, point-in-time recovery, private IP and CMEK for HIPAA workloads. Cloud Spanner for globally distributed, strongly consistent relational data — schema design, query tuning and interleaved table structures included.
Org-policy constraint library covering resource location, public IP prevention, required labels and service enablement. IAM bindings managed in Terraform with least-privilege service accounts, Workload Identity for GKE pods and Secret Manager for credential distribution — no long-lived key files in code.
Cloud Monitoring dashboards and alerting policies, Cloud Logging sinks to BigQuery for long-term retention, Cloud Trace for distributed latency analysis, Error Reporting integration and FinOps dashboards with per-label cost allocation, budget alerts and committed-use discount recommendations.
Stack
GKE (Autopilot + Standard), Cloud Run, Cloud Functions, BigQuery, Cloud SQL (PostgreSQL/MySQL), Cloud Spanner, Firestore, Pub/Sub, Cloud Storage, Cloud Load Balancing, Cloud CDN, Terraform, Config Connector, Cloud Build, Artifact Registry, IAM, Secret Manager, VPC Service Controls, Cloud Monitoring, Cloud Logging, Cloud Trace, Assured Workloads.
Compliance
GDPR EU residency · HIPAA BAA available · SOC 2 Type II · VPC-SC data perimeter
Cases
Production social platform — App Store + Google Play, live across the US and EU — with geo Radar, encrypted messaging and a virtual economy.
Three-app ride-hailing platform — driver, passenger, dispatcher — with real-time GPS, document verification, dual cash/card payments.
Cross-platform sports news app and web portal — Telegram-bot CMS instead of a custom admin, Markdown publishing pipeline.
Why YuSMP
We do not bolt GCP on top of a generic cloud playbook. GKE cluster design, BigQuery schema, Pub/Sub topic topology and IAM hierarchy are designed together — so each layer reinforces the others rather than working around them.
Assured Workloads, VPC Service Controls, CMEK key rings and org-policy constraints are provisioned in the same Terraform modules as application infrastructure — not added after an audit finding. Evidence artefacts for SOC 2 and GDPR audits are generated automatically.
We deliver runbooks, Architecture Decision Records, cost dashboards and on-call playbooks alongside the infrastructure. Your engineering team can operate what we build from day one — not after a lengthy knowledge-transfer engagement.
FAQ
GCP is strongest for data-intensive workloads (BigQuery, Dataflow, Vertex AI), Kubernetes-native teams (GKE is the reference Kubernetes implementation) and organisations that already use Google Workspace. AWS leads on breadth of managed services and US government compliance. Azure is the default for Microsoft-stack organisations (Active Directory, .NET, Office 365). We document the choice as an ADR and base it on your existing tooling, compliance requirements and workload profile.
Autopilot removes node pool management, auto-provisions resources per pod request and is billed per pod rather than per node — lower operational overhead and often lower cost for variable workloads. Standard gives full control over node configuration, DaemonSets, host-path volumes and GPUs — necessary for specialised workloads. We evaluate your workload requirements before cluster provisioning and document the decision so it can be revisited as needs change.
We enforce partitioned tables with required partition filters (preventing full-table scans), cluster tables on high-cardinality filter columns, use materialised views for repeated aggregations and monitor bytes-billed per query via INFORMATION_SCHEMA. For predictable workloads we right-size slot commitments (Standard or Enterprise editions) over on-demand pricing. Budget alerts and billing anomaly detection fire before a runaway query becomes a surprise invoice.
Yes. Google Cloud signs a HIPAA Business Associate Agreement covering GKE, Cloud SQL, Cloud Storage, Pub/Sub, BigQuery and other services. We scope HIPAA workloads to dedicated GCP projects with CMEK encryption for PHI at rest, VPC Service Controls data perimeters, private networking (no public IPs on database instances), immutable Cloud Audit Logs and automated SCC security findings routed to your compliance team.
We combine three layers: Assured Workloads EU Regions and Support folder configuration (restricts personnel and data to EU), org-policy resource location constraints blocking resource creation outside approved EU regions, and VPC Service Controls perimeters preventing API calls that would move data to US services. Residency is verified at Terraform plan time and monitored continuously via Security Command Center. CMEK key rings are provisioned in EU regions so encryption keys never leave the EU.
Cloud Run for stateless HTTP and event-driven workloads with variable traffic — faster time-to-production, zero node management, per-request billing and built-in HTTPS. GKE for stateful workloads, long-running background jobs, DaemonSet-based agents, GPU inference or teams that need full Kubernetes ecosystem tooling (Helm, Argo CD, Istio). Many architectures use both: GKE for the core platform, Cloud Run for lightweight auxiliary services and scheduled jobs.
We start with a discovery phase — inventory of services, data volumes, compliance requirements and dependency mapping. Stateless services are containerised and deployed to Cloud Run or GKE. Databases are migrated using Database Migration Service (PostgreSQL, MySQL) or Datastream for ongoing replication with minimal downtime. Data warehouses move to BigQuery via Dataflow or Transfer Service. Typical timeline: discovery two weeks, proof-of-concept four weeks, production cutover eight to sixteen weeks depending on complexity and data volume.
Response within 1 business day. NDA on request.
