The Reddit user who asked about dynamic columns in Power Query has put their finger on a problem that has quietly cost data professionals thousands of hours: brittle queries that break the moment a source file adds a new column or drops an old one. Our view is straightforward: if your workflow depends on rigid column names, you are fighting your tools instead of using them. The solution is not to find a single static workaround, but to embrace the dynamic nature of Power Query itself.
The standard approach, selecting specific columns by name, works only when you control the source schema. In real-world data work, you rarely do. A monthly report from a vendor, an exported CRM table, or a database view that gets updated by another team: any of these can introduce or remove columns without warning. The result is a query that errors out, and you spend time debugging instead of analyzing. The fix is to shift your thinking from "these are the columns I need" to "this is the data I want to keep." Use Power Query's table schema features, like `Table.SelectColumns` with a list of column names stored as a parameter, or `Table.RemoveColumns` to drop known noise columns while letting everything else pass through. Better yet, use `Table.ColumnNames` to inspect the incoming structure and write logic that adapts. If a column called "Sales_Q1" appears one month and "Sales_Q2" the next, your query should handle both without manual intervention.
This is not about writing complex M code that only a specialist can maintain. It is about building queries that treat column names as data, not as fixed anchors. Start by identifying which columns are truly essential to your output. For everything else, let the query be flexible. Use `Table.SelectRows` with a condition based on column existence, or use `Table.TransformColumnTypes` with a function that only applies to columns present in the current table. These techniques are documented, testable, and they scale across dozens of sources without a rewrite each time.
The real takeaway is that your workflow should not be fragile. When you design for change, you stop being surprised by it. Next time you import a file and a column has vanished, your query should not break, it should adapt. That is the difference between a tool you manage and a tool that manages your data for you. Build the adaptability in now, and save yourself the frustration later.