During a transformation task, what is the advantage of avoiding the buffering of all files into memory?

Avoiding the buffering of all files into memory primarily reduces memory consumption, which is crucial during transformation tasks, especially when dealing with large datasets. When all files are buffered into memory, it can lead to high memory usage, potentially resulting in exhaustion of system memory and subsequent performance degradation or even failure of the task. By processing data in smaller chunks or streaming it gradually, memory usage is kept at manageable levels, allowing the system to efficiently handle larger volumes of data without straining the memory resources.

Additionally, this approach adds to the robustness of the data processing pipeline, making it more scalable. Although memory efficiency is a significant aspect, it also indirectly supports other factors like processing speed and code complexity, but the most direct and immediate advantage is the reduction in memory consumption. This strategy is particularly important in environments where resources may be limited or where the volume of data exceeds the capacity of available memory.