What does incremental processing allow in a data integration workflow?

Incremental processing is a strategic approach in data integration workflows that focuses on optimizing performance and resource usage. It allows for the identification of new or updated records since the last execution of a process. This means that instead of processing the entire dataset every time, which can be time-consuming and resource-intensive, only the changes made since the last processing run are captured and processed.

By integrating this method, data workflows are made more efficient, as they significantly reduce the amount of data that needs to be moved, transformed, or loaded, improving overall system performance and minimizing processing load. This is particularly useful in scenarios where datasets are large and frequent updates occur, allowing organizations to maintain up-to-date information without the burden of full dataset processing while ensuring timely data availability.

Other options reflect different concepts not central to the essence of incremental processing. Processing the entire dataset is contrary to the efficiency goal of incremental processing. Excluding null records does not directly relate to incremental updates and instead pertains to data cleansing. Simplified data formatting involves the structuring of data rather than focusing on incremental changes, which is not the primary function of this processing approach.