Within ETL, which description correctly reflects a form of data transformation commonly performed before loading to the warehouse?

Introduction / Context:Transformation is the “T” in ETL, where data is cleansed, standardized, enriched, and sometimes aggregated. Aggregation is a common transformation when preparing summary tables or materialized views.

Given Data / Assumptions:

• We are not reversing summaries back into details (rare or impossible without detail).
• We focus on typical warehouse transformations (e.g., rollups, derived metrics).
• Join/split phrasing in options C/D is incorrect: you join multiple sources together, not “into various sources.”

Concept / Approach:Aggregations (daily to monthly, line-level to order-level) reduce granularity to accelerate queries and support reporting. Other transformations include data type normalization, code mapping, deduplication, and SCD handling.

Step-by-Step Solution:

Verification / Alternative check:ETL tool features and SQL transformations heavily support GROUP BY aggregations to create summary tables.

Why Other Options Are Wrong:Disaggregation: Requires additional detail; not a standard transform. Joining/splitting wording: Inaccurate and not typical transformation phrasing.

Common Pitfalls:Over-aggregating early and losing necessary detail for unforeseen analytics.

Final Answer:Changing data from a detailed level to a summary level (aggregation)