When working with datasets built by a schedule in Foundry, what is a common purpose of intermediate datasets?

Intermediate datasets play a crucial role in the data processing pipeline within Foundry by facilitating collaboration between different stages of data analysis. These datasets serve as transitional data points, allowing different teams or tools to work on specific segments of data without disrupting the entire workflow.

For instance, when data is being transformed or aggregated, intermediate datasets can capture the results of those transformations at various steps. This makes it easier for analysts or data scientists to share insights, validate outputs, and build on one another's work. By providing a structured way to handle data in stages, intermediate datasets enhance the overall efficiency and clarity of data projects, leading to more cohesive analysis and improved decision-making.

The options suggesting that intermediate datasets are used solely for data export or that they are consumed only by external tools do not capture the collaborative and iterative nature of data workflows that intermediate datasets support. Additionally, while providing historical data analytics is an important function in some contexts, it does not encompass the primary role of intermediate datasets, which is more focused on facilitating process integration and teamwork.