Which functionality allows connectors to manage large data sets effectively?

Connectors in Boomi utilize automatic pagination to manage large data sets effectively. This functionality allows the system to break down large data sets into smaller, more manageable chunks or pages. When working with extensive data volumes, retrieving all data at once can lead to resource strain and performance issues. Automatic pagination helps mitigate these challenges by fetching data in segments, thereby enhancing both processing efficiency and system performance.

Using pagination, the connector can iteratively retrieve and process subsets of records, which not only optimizes memory usage but also improves response times and reduces the likelihood of timeouts or failures during data processing. This approach is particularly valuable when dealing with APIs and databases that limit the amount of data returned in a single request, ensuring that large data sets are handled smoothly and effectively.

The other options do not offer the same level of efficiency and scalability when managing large amounts of data. Manual data entry is impractical for large data sets, and limiting data to single records does not address the need for processing multiple records. Support for large files only may suggest handling larger files but does not inherently provide mechanisms to manage data volume effectively within operations.