Hello shubham,

Great to hear you’ve already got the basic checks working! You're right—the GitHub link doesn’t provide much detail for more advanced rules like SQL expressions or regex. For these, I’d suggest using the SQL_EXPRESSION type in your rule config, which lets you write custom Spark SQL logic. For example, for a regex check on emails, you can use something like "sql_expression": "email RLIKE '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\\\.[a-zA-Z]{2,}$'". For date checks, you can compare against the current date like "sql_expression": "event_date <= current_date()" or "event_date > current_date()" for future dates. These expressions can be tested directly in a notebook before adding them to your DQX config. If you'd like, I can help you put together a few sample rules—just let me know the exact columns and checks you're trying to build. Happy to help!

Happy Weekend!

Regards,

Brahma