For a simple and maintainable process, the best approach is to use the native Databricks SQL (DBSQL) Queries feature combined with Tags and Workspace Folders.

Here is how you can set up that workflow:

1. Save them directly in the Databricks SQL > Queries section. Create a dedicated folder structure in your Workspace (e.g., Shared/Ad-hoc_Requests/Finance or Shared/Ad-hoc_Requests/Marketing) to keep them out of personal user folders.

2. Use the native Tags feature on the query in the DBSQL UI. You can create a standardized tagging system like:

• Department: finance, sales

• Requester: req:john_doe

• Status: active, one-off, deprecated

Pro-tip: Put a standard comment block at the very top of the SQL code for the details:

/*
Requester: John Doe (Marketing)
Ticket: JIRA-1234
Purpose: Extract Q3 churned users for email campaign
Owner: Data Team
*/
SELECT ...

3. Because you are using tags, you can easily filter the Queries UI by the one-off or deprecated tags and delete them quarterly. You can also look at the "Last Run" column in the Queries list to quickly spot queries that haven't been touched in months.

4. If your team wants version control later, you can save these as .sql files inside Databricks Git Folders (Repos). However, if your main goal is simplicity right now, just stick to the DBSQL Queries UI with strict tagging and folder management.