BigQuery
BigQuery. Is Google Cloud’s fully managed, serverless data platform for SQL analytics, ML, and BI at petabyte scale.
Blurb
BigQuery is the autonomous data and AI platform, automating the entire data life cycle so you can go from data to AI to action faster.
Summary
What it is: Columnar warehouse with standard SQL, partitioned tables, streaming inserts, BigQuery ML, and native ties to Looker, Vertex AI, and GCP IAM. Pay per query and storage; slot reservations for steady workloads.
When to use: Large analytics on GCP; federated queries over GCS objects; marketing and product event pipelines landing in tables; teams already standardized on dbt-core against BigQuery.
When to skip: Primary OLTP (use Postgres). Multi-cloud strategy that forbids GCP-only warehouses. Small datasets where Postgres replicas plus Grafana or Metabase suffice.
Key features: Serverless scaling, nested and repeated fields, sharing and authorized views, scheduled queries, cost controls via quotas and reservations.
Details
| Topic | Notes |
|---|---|
| Deploy | GCP project and dataset IAM; no VMs to patch |
| Cost | Monitor bytes scanned; partition and cluster tables; use cached results |
| BI | First-class in Metabase, Redash, Apache Superset |
Practices: Separate dev and prod projects; avoid SELECT * on wide tables; load via batch or streaming with clear retention; pair with Postgres for transactional source of truth.
References