Firstly quokka is pure Python, which I believe is good for interoperability with Python based UDFs.

Secondly quokka tries to be fault tolerant. I.e. it can handle worker failures intra query and not have to start over. This is quite important in real world spark deployments with thousands of nodes running many hours. AFAIK this is not well supported by most spark alternatives.

Finally quokka has a much stronger focus on time series data analytics. It is meant to excel at workloads like range joins and asof/PIT joins used for feature engineering. (This part isn't too stable yet so is not open source)

This means quokka optimizes on a different point in the UDF/performance/fault tolerance tradeoff space than something like arrow ballista or starrocks, which I think are pure performance plays