Which of the following patterns best describes the process of combining data from different systems into one?

The aggregation pattern is the most appropriate choice for describing the process of combining data from different systems into one. This pattern is specifically designed to collect and unify data from multiple sources, allowing it to be processed, analyzed, or presented as a single, comprehensive dataset. In integration scenarios, aggregation is crucial for creating a more holistic view of the information, enabling better decision-making and insights.

When implementing an aggregation pattern, various methods can be used to consolidate data, such as combining datasets through transformations, filtering, or even applying business logic to derive new information. This ensures that the resulting dataset serves its intended purpose effectively, whether it's for reporting, analytics, or further processing.

In contrast, other patterns like correlation, migration, and bi-directional sync serve different purposes. Correlation focuses on establishing relationships between data points, migration deals with transferring data from one system to another, and bi-directional sync ensures that changes in one system are reflected in another. These patterns do not prioritize the consolidation of data from various sources in the way that the aggregation pattern does.