AnalyticsPOC data load requirement: Which statement describes the rule for updating data before populating the dimensional model?

AnalyticsPOC data load requirement: Which statement describes the rule for updating data before populating the dimensional model?

• A. Data loads must update raw and cleansed data completely before populating the dimensional model.
• B. Data loads can be partial and skip cleansing.
• C. Data must be loaded in a single batch only.
• D. Data must be loaded as XML.

Answer: (A)

The critical rule is to update both raw data and cleansed data completely before populating the dimensional model. This ensures the dimensional model is built from a fully refreshed and consistent set of data, with transformations and business rules already applied. If updates are partial or cleansing is skipped, you risk data quality issues, mismatched keys, and incomplete dimensions or facts, which can lead to incorrect analyses and unreliable reporting. A full update of both layers provides a reliable foundation for the dimensional model, where everyone relies on clean, synchronized data. Options that suggest partial updates, a single batch constraint, or a specific data format (like XML) don’t address the need for a complete, consistent data state before loading the model.

The critical rule is to update both raw data and cleansed data completely before populating the dimensional model. This ensures the dimensional model is built from a fully refreshed and consistent set of data, with transformations and business rules already applied. If updates are partial or cleansing is skipped, you risk data quality issues, mismatched keys, and incomplete dimensions or facts, which can lead to incorrect analyses and unreliable reporting. A full update of both layers provides a reliable foundation for the dimensional model, where everyone relies on clean, synchronized data. Options that suggest partial updates, a single batch constraint, or a specific data format (like XML) don’t address the need for a complete, consistent data state before loading the model.