What should be considered for optimal integration performance when using multiple API calls?

Using larger batch sizes can indeed reduce the number of API calls made, which is crucial for optimizing integration performance. When integrating systems, each API call typically involves overhead such as establishing a connection, authentication, and data processing. By batching requests, you can minimize this overhead significantly. This is particularly beneficial in high-volume environments where the number of API queries can quickly add up, leading to potential throttling and performance bottlenecks. Optimizing batch sizes allows you to send more records in each call, reducing the total number of calls needed and improving overall throughput.

In contrast, while executing all calls synchronously may ensure data consistency, it can lead to performance degradation, particularly if one call takes longer than expected, thus holding up the entire integration process. Reducing the number of fields queried can improve performance to an extent, but it primarily affects the size of the data returned rather than the number of calls. Parallel processing can enhance performance in situations where tasks are independent; however, if not managed correctly, it can also lead to increased load and potential rate limiting. Hence, the choice of using larger batch sizes stands out as a more reliable strategy for enhancing integration performance.