In DAX, the key data types are quite straightforward but understanding how they behave internally is really important for writing efficient calculations. The main types supported are Numeric, Text, Boolean, Date/Time, and Blank.
In practice, most of my work with DAX involves Numeric and Date/Time types, especially for measures like sales, profit margins, or time intelligence calculations. For example, when I calculate year-to-date sales using TOTALYTD, DAX automatically recognizes the column as a Date/Time type and aggregates values accordingly.
One thing I’ve noticed while working on Power BI reports is that DAX is quite flexible — it does implicit type conversions when needed. For instance, if I compare a number with a text that represents a number (like "100"), DAX tries to convert it to a numeric type before comparison. While this is convenient, it can sometimes cause unexpected results if the data isn’t clean. I faced this once when importing data from Excel — some numeric columns had blank cells or text placeholders like “N/A”, which made DAX treat the entire column as text, leading to errors in aggregation. I solved it by cleaning and converting data in Power Query before loading it into the model.
A limitation I’ve seen is that DAX doesn’t have complex types like arrays or objects, so operations on collections or lists aren’t as direct as in programming languages. To handle such scenarios, I use table functions like SUMMARIZE or ADDCOLUMNS to simulate structured data.