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Cloud · GCP

BigQuery

Mediumcloud-07
gcpbigquerywarehousepartitioningcost

Question

What is BigQuery, and how do data engineers use it?

Solution

BigQuery is Google's serverless cloud data warehouse. You load or query data with SQL; Google manages storage and compute. You typically pay for storage plus bytes scanned (on-demand) or for slot reservations.

GCS / Pub/Sub / Datastream
           |
           v
        BigQuery datasets.tables
           |
           +--> Looker / BI / dbt / notebooks

Why it feels different from classic warehouses

  • No cluster nodes to resize for basic use
  • Columnar storage + separation of storage and compute
  • Partitioning (often by date) and clustering cut scan cost
  • External / federated queries can read GCS or other sources

Tiny cost habit

-- Prefer partitioned filters so you do not full-scan
SELECT COUNT(*)
FROM sales.fct_orders
WHERE order_date BETWEEN '2026-09-01' AND '2026-09-05';

DE workflow

1. Land raw files in GCS 2. Load or query into BigQuery staging tables 3. Transform with SQL/dbt into marts 4. Govern with IAM datasets and authorized views

Interview tip: "BigQuery = serverless GCP warehouse; cost follows scanned bytes unless you reserve slots." Mention partitioning/clustering as the fresher cost lever.

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