In data transformation during ETL, what can a “multifield transformation” accomplish when mapping source fields to target fields?

Introduction / Context:
Transformation logic adapts data from source systems to target warehouse schemas. Multifield transformations are versatile mappings that restructure data for analytics, conformance, and normalization or denormalization.

Given Data / Assumptions:

• Source and target schemas often differ in granularity and shape.
• Transformations can split, merge, and reshape attributes.
• Business rules may dictate how fields are combined or decomposed.

Concept / Approach:
A multifield transformation can split one field (for example, full_name) into several (first_name, last_name), merge several fields (street, city, state) into one address_line, or map multiple to multiple with complex derivations. Therefore, all listed possibilities are valid capabilities.

Step-by-Step Solution:

Verification / Alternative check:
ETL tools (for example, Informatica, DataStage, SSIS) explicitly support split, merge, and multi-column mapping transformations.

Why Other Options Are Wrong:

• Options A/B/C are partial cases; D correctly aggregates all valid cases.

Common Pitfalls:
Hard-coding parsing rules without handling edge cases (for example, middle names, international addresses); always validate with profiling.

Final Answer:
All of the above