Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Typical stack**: GCS (raw/curated storage) + Dataflow (streaming/batch) + BigQuery (analytics) + Cloud Composer (orchestration) + Data Catalog (governance). **Ingest**: Pub/Sub, Transfer Service, Cloud Storage. **Processing**: Dataflow (Beam), Dataproc (Spark). **Analytics**:...
Typical stack: GCS (raw/curated storage) + Dataflow (streaming/batch) + BigQuery (analytics) + Cloud Composer (orchestration) + Data Catalog (governance). Ingest: Pub/Sub, Transfer Service, Cloud Storage. Processing: Dataflow (Beam), Dataproc (Spark). Analytics: BigQuery, Looker. Governance: Data Catalog, DLP. Why GCP: Strong integration (BigQuery + GCS native); serverless options; good ML (Vertex AI). Scalability: All scale elastically. Cost: BigQuery and Dataflow are pay-per-use; over-provisioning Dataproc is costly. Best practice: Leverage managed services; use VPC-SC and CMEK for enterprise security; partition and cluster BigQuery tables.
Pro-Move: 'We use Data Catalog for discovery and Dataflow templates for reusable pipelines—same patterns across 20+ datasets.' Red Flag: Listing tools without explaining why you chose them or how they integrate—suggests surface-level experience.
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