Why are iterator UDFs preferred for processing large datasets?

Iterator UDFs are preferred for processing large datasets primarily because they minimize the overhead associated with loading models multiple times. When working with large datasets, loading a model into memory for every single processing task can lead to significant inefficiencies and delays. By using iterator UDFs, the model can be loaded once and then applied to batches of data in an efficient manner. This not only speeds up the processing of the dataset but also reduces resource consumption, as the model remains in memory while it processes the incoming data.

This efficient handling of model usage is crucial, especially in big data environments where the cost of loading models repeatedly can be prohibitive. Importantly, this method allows for the smooth operation of workflows where large volumes of data need to be transformed or predictions must be made in an organized and resource-effective way.