DAX filters are more surprising than most users realize, and that's precisely why this deep dive matters. The piece from Towards Data Science does what good technical writing should: it takes a concept many of us think we understand, filtering, and reveals the hidden logic beneath the surface. For anyone who has ever written a CALCULATE statement and wondered why the result didn't match expectations, this is the kind of explanation that turns confusion into clarity.
The real value here isn't just the examples, though they are well-chosen. It's the shift in perspective. Most spreadsheet users treat filters as simple on-off switches: apply a condition, get a subset. DAX filters operate differently, and that difference is where both the power and the frustration live. Filter context interacts with row context, evaluation order can produce counterintuitive results, and a filter that works in one expression might fail in another. These aren't edge cases, they are the mechanics that separate a working model from a broken one.
For the data professionals and analysts in our audience, this matters because it changes how you debug. When a measure returns an unexpected number, the instinct is often to check the data or the formula syntax. A better first step is to examine the filter logic. Are you applying a filter that gets overwritten by a later context? Is your filter being applied to the wrong granularity? The examples make these abstract concepts tangible, and that is exactly the kind of insight that saves hours of frustration.
If you work with DAX regularly, this is worth your time. It doesn't promise to make you an expert overnight, and it doesn't oversell its material. Instead, it offers a clear, practical look at a topic that is often glossed over in tutorials. Read it with your own code open. Try the examples. Then go back to a measure that has been puzzling you and see if the filter logic reveals the problem. That is the only test that matters.