At least in case of Bigquery, I have fought with indeterminist-like issues many times over, especially when dealing with window functions that aggregate floats

At least in case of Bigquery, I have fought with indeterminist-like issues many times over, especially when dealing with window functions that aggregate floats from different compute nodes, where rows cannot be further sorted on a unique column (i.e. the maximum sorting granularity for rows with a similar column of interest to compute a window function over has been reached). Inconsistent results could be resolved by introducing additional out of data constraints (e.g. incremental hashes), but it can take quite a while to figure out at which exact point in a complex query these constraints need to be introduced. Beyond that, some functions might still produce different results between runs, e.g. `ml.tf_idf` and `ml.multi_hot_encoder` that take some approximation liberties. Whether these functions are relational in the traditional sense is up for debate.