What is one recommended practice for handling shared datasets across multiple pipelines in Foundry?

Creating a new pipeline dedicated to building the shared dataset is a recommended practice because it promotes modularity and maintainability within the data engineering workflow. By centralizing the responsibility for building the shared dataset in a single pipeline, you minimize redundancy and ensure consistency across all consuming pipelines. This dedicated pipeline acts as the single source of truth, ensuring that any updates or alterations to the shared dataset are managed in one place, reducing the risk of errors that could occur if multiple pipelines were attempting to build it independently or integrating it directly.

This approach also streamlines collaboration among teams, as changes to the shared dataset can be made and tested in isolation before being made available to other pipelines. Such organization helps in version control, facilitates better debugging, and ensures that all dependent pipelines remain operational without conflict, as they will receive the most up-to-date version of the dataset from the dedicated pipeline. Additionally, treating datasets this way can enhance performance by preventing unnecessary data processing that could occur if multiple pipelines were to each build their own version of the dataset.