How do you decide when to stay with a notebook and when to convert the work into scripts? Is it mainly when the project needs to be automated, reused, scheduled, or shared with others? I’d be interested to hear how people handle this in real projects.
For those who have worked with PostgreSQL, MySQL, and SQL Server, what’s a feature or behavior that surprised you because it worked differently between them?
It could be anything like date functions, string handling, LIMIT/TOP, NULL behavior, JSON, window functions, or even error handling.
What difference caused you the most trouble, and how did you learn to handle these database-specific differences?
Sometimes the solution might be increasing the warehouse size, but other times the real problem could be the SQL itself — inefficient joins, unnecessary data scans, poor filtering, etc.
How do you normally determine which one is actually causing the problem?
Do you look at query profiles, execution plans, scanned data, warehouse utilization, or other metrics before deciding what to change?
They both seem like they divide data into groups, so I’m having trouble understanding when I should use one instead of the other.
What are the main things that can cause this difference?
I know the basics of Power BI, including Power Query, data modeling, DAX, and creating dashboards. I want to improve my skills and become confident working on real-world projects.
What should I focus on next? Should I learn advanced DAX, performance optimization, Power BI Service, deployment, or something else?