A data warehouse is designed primarily to supportanalytics, reporting, and decision-makingrather than day-to-day transaction processing. Operational systems are optimized for fast inserts/updates and real-time business operations such as order entry, billing, or customer service workflows. In contrast, a warehouse consolidates data—often from multiple sources—into structures optimized for querying, trending, and historical analysis. From a cybersecurity and governance perspective, this distinction matters because warehouses frequently contain large volumes of aggregated, historical, and sometimes sensitive information, which can increase impact if confidentiality is breached. As a result, controls like strong access governance, role-based access, least privilege, segregation of duties, encryption, and audit logging are emphasized for warehouses to reduce insider misuse and limit exposure.
Option B is false because warehouses often use different structures (for example, dimensional models) than production systems, specifically to improve analytical performance and usability. Option C can be true in some architectures, but it is not universally required; organizations may operate multiple warehouses, data marts, or lakehouse patterns, and not all operational data is appropriate to centralize due to privacy, cost, and regulatory constraints. Option D is incorrect because cleansing is commonly performed in dedicated integration pipelines and staging layers rather than changing operational systems to “pre-clean” data. Therefore, A is the best verified statement.
