Normalization Trade-offs — Read-Only vs. Updateable Databases For several practical reasons (performance, simplicity, and reporting patterns), normalization is not often an advantage for a(n) ________ database.

Introduction:
Normalization is invaluable for transactional systems where data changes frequently. In read-only or reporting databases, however, denormalized structures often serve analytics better. This question asks you to identify where normalization is not typically advantageous.

Given Data / Assumptions:

• Read-only databases prioritize fast reads and simplified queries.
• Updateable databases prioritize correctness during inserts/updates/deletes.
• Join-heavy, fully normalized schemas can slow reporting use cases.

Concept / Approach:
Normalization reduces redundancy at the cost of more joins. For read-only analytic workloads, denormalized or dimensional models (star/snowflake) minimize joins and ease reporting. Thus, full normalization delivers less benefit in read-only contexts than in OLTP scenarios, where anomalies must be strictly controlled.

Step-by-Step Solution:
1) Identify workload: read-mostly analytics versus update-heavy OLTP.2) Map modeling goals: analytics favor simpler queries and fewer joins.3) Conclude that normalization is not often an advantage for read-only databases.

Verification / Alternative check:
Data warehouses routinely adopt dimensional models to accelerate aggregations and improve usability, corroborating the limited value of strict normalization in read-only settings.

Why Other Options Are Wrong:

• Updateable: Normalization is very beneficial to prevent anomalies.
• Either / None / Streaming: Overly broad or off-target relative to the question focus.

Common Pitfalls:
Assuming one model fits all workloads. Align normalization level with workload characteristics.

Final Answer:
read-only