A warehouse model must balance query speed and storage. What do you decide?
Why A is the answer
The degree of normalization
The degree of normalization is the choice that balances query speed against storage.
In detail
- Normalization trades query speed against storage.
- BigQuery often favors denormalized designs.
Use case
An illustrative scenario, not a real incident.
A warehouse keeps a star schema with some flattened tables, which balances speed and storage.
See it in different ways
stateDiagram-v2 [*] --> QueryPlan QueryPlan --> PartitionAndCluster: scans too much QueryPlan --> MaterializedView: repeated aggregation QueryPlan --> BiEngine: dashboards need sub-second PartitionAndCluster --> [*] MaterializedView --> [*] BiEngine --> [*]
flowchart TD
Q{"What matters more?"}
Q -->|"Query speed"| A0["Denormalize more"]
Q -->|"Less duplication"| A1["Normalize more"]Key point: Normalization balances speed and storage.
Why the other options are wrong
- B. The Pub/Sub topic name
- A topic name is unrelated.
- C. The Airflow environment size
- Airflow environment size is unrelated.
- D. The Pub/Sub retention period
- Retention is unrelated to warehouse design.