  Extra-large datasets are becoming increasingly accessible, and computing
tools designed to handle huge amount of data efficiently are democratizing
rapidly. However, conventional statistical and econometric tools are still
lacking fluency when dealing with such large datasets. This paper dives into
econometrics on big datasets, specifically focusing on the logistic regression
on Spark. We review the robustness of the functions available in Spark to fit
logistic regression and introduce a package that we developed in PySpark which
returns the statistical summary of the logistic regression, necessary for
statistical inference.
