When is the augment transformation particularly useful?

The augment transformation is particularly useful when matching record data from two related datasets because it allows you to enrich a primary dataset with additional attributes from a secondary dataset. This enhancement is crucial when trying to create a more detailed view by adding relevant information or attributes that can provide context or additional insights.

In practical terms, using the augment transformation effectively combines data that correlates based on a common key or identifier, ensuring that when you analyze or visualize the primary dataset, you have all relevant information without duplicating records. This is especially common in scenarios where you want to combine demographic information with sales records or product details from different sources.

The other options do not align with the primary purpose of the augment transformation. Concatenating text fields involves simply merging strings without enhancing data quality or context, generating random data serves a different purpose mainly for testing without utilizing existing datasets, and calculating averages relies on direct mathematical operations rather than enriching data records through a relational approach.