How does splitting source files help maintain good performance in Synapse Analytics?

Splitting source files contributes significantly to maintaining good performance in Synapse Analytics primarily through the alignment of compute nodes with storage segments, which enhances data processing efficiency. When data is split into smaller files, it allows the system to distribute the workload more evenly across multiple compute nodes. Each node can then process its designated segment of data concurrently, leading to faster execution times and improved resource utilization.

This design is advantageous because it reduces bottlenecks that might occur when larger files are processed. With smaller, more manageable file sizes, the throughput of data can increase as compute resources can work in parallel rather than getting stuck on a single large dataset. Moreover, this segmentation allows Synapse to better leverage its distributed architecture, ensuring that each compute node operates on the data that resides on or near its own local storage, thereby minimizing data movement and further improving performance.

While the other options touch on important aspects of data management, they do not encapsulate the primary performance benefits of file splitting in the context of Synapse Analytics as effectively as the alignment of compute nodes with storage does.