In DSS 3.0, DSS was upgraded to Pandas 0.17, which indeed introduces a behavior change regarding fillna on date columns.
* In DSS 2.3 / Pandas 0.16, filling a date column with "" filled the column with the "NaT" value ("Not a time") and kept the dtype - filling with "anyotherstring" failed
* In DSS 3.0 / Pandas 0.17, filling a date column with any string, whereas empty or not-empty now triggers a downcast of the column to object, which DSS then interprets as a string column
Filling a whole dataframe, containing mixed value types, with a single value is inherently dangerous. Both behaviors of Pandas are questionable, but in fine, you'd probably want to fillna only the columns for which it makes sense, with a properly-typed value