In Unity Catalog, ownership is the primary mechanism for delegating full administrative control of a securable object. The owner of a schema can grant and revoke privileges on that schema and on all objects contained within it (such as tables and views), without needing to be a workspace admin or metastore admin.
Transferring ownership of main.marketing to the mkt-admins group therefore aligns precisely with the requirement to delegate day-to-day permission management at the schema scope. Granting MANAGE at the metastore level (option B) would be overly permissive, enabling global administration across all schemas and objects, which violates the principle of least privilege. Simply granting USE SCHEMA and MODIFY (option C) does not confer the ability to manage grants for other users. Making the group workspace admins (option D) unnecessarily elevates privileges beyond Unity Catalog's data governance model. Unity Catalog documentation emphasizes using ownership transfer to delegate administrative responsibilities at the appropriate scope while maintaining centralized governance in Databricks.
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This approach would allow the data engineering team to use the existing SQL program and add some logic to control the execution of the final query based on the day of the week. They could use the datetime module in Python to get the current date and check if it is a Sunday. If so, they could run the final query, otherwise they could skip it. This way, they could schedule the program to run every day without changing the data model or the source table. References: PySpark SQL Module, Python datetime Module, Databricks Jobs

Databricks cluster pools are a set of idle, ready-to-use instances that can reduce cluster start and auto-scaling times. This is useful for scenarios where a data team needs to run an automated report as quickly as possible, without waiting for the cluster to launch or scale up. Cluster pools can also help save costs by reusing idle instances across different clusters and avoiding DBU charges for idle instances in the pool. References: Best practices: pools | Databricks on AWS, Best practices: pools - Azure Databricks | Microsoft Learn, Best practices: pools | Databricks on Google Cloud