What role do 'intermediate' datasets play in a Foundry data pipeline schedule?

Intermediate datasets in a Foundry data pipeline schedule serve a specific and essential purpose—they are built by the schedule and then used by other datasets within the same schedule. Their primary function is to act as transitional outputs that provide necessary data transformations or aggregations, which can then feed into subsequent processes or datasets.

By building these intermediate datasets, the pipeline can modularize its data processing, allowing complex transformations to be broken down into simpler, more manageable steps. This modular approach not only enhances readability and maintainability of the data pipeline but also ensures that intermediate results can be reused effectively, contributing to improved efficiency and reduced computation time.

In contrast, the other options describe scenarios that do not align with the definition or role of intermediate datasets as recognized in data pipelines. For instance, datasets that are not built by the schedule or that stand alone without being utilized in subsequent operations do not fulfill the characteristics of intermediate datasets, which are inherently reliant on their integration within the workflow of the pipeline.