Google Cloud
Data & Analytics
intermediate
Warehouse & BI Serving — Google Cloud Architecture Template
Nightly ELT from Cloud SQL and flat files through Data Fusion into a BigQuery warehouse that serves BI dashboards.
Why this architecture works
- ELT into BigQuery pushes transformation to the warehouse, exploiting its cheap parallel compute.
- Data Fusion pipelines are visual and versioned, making the ETL auditable by analysts.
- Cloud Scheduler keeps loads on a predictable cadence with alerting on missed runs.
- Authorized views and dataset-level IAM expose only governed data to BI tools like Looker Studio.
What's inside (8 resources)
OLTP Source
gcp-cloud-sql
Flat File Drops
gcp-cloud-storage
Data Fusion ELT
gcp-data-fusion
Nightly Trigger
gcp-scheduler
BI Warehouse
gcp-bigquery
Dataset IAM
gcp-iam
Pipeline Logs
gcp-logging
Cloud Monitoring
gcp-monitoring
From template to running infrastructure
- Open this template in the CloudForge visual designer (free account, no credit card).
- Customize resources, names, and connections on the drag-and-drop canvas — or ask Vani, the AI architect, to adapt it.
- Generate production-ready Terraform or Pulumi in one click.
- Review the plan diff and security scan, then deploy with human approval.