Generalized Linear Modeling is the sliced bread of Data Science. It's transparent, it's flexible and allows for response variables to be of different distributions and connected to the model by different link functions. In this talk we present an implementation of Distributed GLM in OpenSource Math Engine, H2O. We also take a peek into Regularization & ADMM - a technique that's been popularized by Stephen Boyd and gaining ground amongst data science practitioners. We tie the theory up with a showcase of the power of GLM on 16-nodes over all 20 years of Airline Flight data predicting which airports to avoid in your upcoming summer travels!