Which data integration pattern focuses on ongoing synchronization from one to many systems?

The correct choice emphasizes the concept of "Broadcast," which is a data integration pattern designed for ongoing synchronization across multiple systems. In a broadcast pattern, data from a single source is duplicated and sent out to multiple target systems simultaneously. This ensures that all systems have access to the same data in real time or near real time, facilitating a consistent data state across all locations.

The broadcast pattern is particularly useful in scenarios where data needs to be shared widely, such as distributing updates to inventory levels, customer information, or other critical data elements that different systems rely on. This method effectively keeps all subscribing systems synchronized with the latest information without each system needing to directly query the source for updates.

In contrast, other patterns like migration focus on moving data from one system to another, typically for a one-time operation, while aggregation combines data from multiple sources into one system, which is not aimed at ongoing synchronization. Correlation involves linking related data together, but again does not emphasize synchronizing data across multiple systems. Hence, broadcast is the most suitable choice for ongoing synchronization from one to many systems.