You need to schedule the population of the medallion layers to meet the technical requirements. What should you do?

A common and best practice for orchestrating complex data workflows, such as populating medallion layers (Bronze, Silver, Gold), is to use a master pipeline that triggers child pipelines. This allows for modularity, reusability, and easier management of dependencies between different stages of data processing.

Scheduling a single notebook might be suitable for simple, isolated tasks but lacks the orchestration capabilities and dependency management needed for multi-stage medallion layer population.

Scheduling an Apache Spark job directly without a pipeline wrapper does not provide the high-level orchestration, scheduling, and monitoring features of a data pipeline for managing multiple medallion layers.

While multiple data pipelines could be scheduled, scheduling them independently makes dependency management and sequential execution across medallion layers more complex than a master-child pipeline structure.