Read-only database design: do guidelines differ because updates never occur?

Introduction / Context:
Read-only databases—such as reporting marts or historical archives—have different operational characteristics than online transaction processing (OLTP) systems. Since updates do not occur, designers can adopt guidelines that emphasize fast reads, summarization, and precomputation without worrying about update anomalies.

Given Data / Assumptions:

• Workload is dominated by complex queries and aggregations.
• No user-initiated inserts/updates/deletes occur during normal operation.
• Data may be loaded in batches and then queried extensively.

Concept / Approach:
In read-only contexts, denormalization (for example, star schemas), materialized views, pre-aggregated tables, and additional indexes are common to accelerate analytics. Constraints may still be defined but are oriented toward ensuring load-time quality rather than enforcing transactional integrity during writes. Partitioning and columnar storage may be preferred for scan-heavy workloads.

Step-by-Step Solution:

Verification / Alternative check:
Benchmark representative analytical queries on normalized vs. denormalized schemas. Read-only designs typically show markedly better performance with denormalization and appropriate indexing.

Why Other Options Are Wrong:

• Claiming identical priorities ignores the lack of updates and different performance goals.
• Index status, hardware speed, or storage format alone do not define the design philosophy.

Common Pitfalls:
Over-denormalizing without documenting lineage; allowing uncontrolled duplication that complicates refreshes; forgetting to enforce integrity during data loading.

Final Answer:
Valid (read-only designs prioritize query performance and may denormalize)