Recent advances in computing and sensor technologies have pushed the amount of data we collect or generate to limits previously unheard of. This paper focuses on the big data challenges that building modeling experts may face in data collected from a large array of sensors, or generated from running a large number of building energy/performance simulations. A case study is presented that highlights the technical difficulties that were encountered and overcome in order to run 3.5 million EnergyPlus simulations on supercomputers and generating over 200 TBs of simulation output. While this is an extreme case, it led to the development of technologies and insights that will be beneficial to modelers in the immediate future. The paper discusses the different data management technologies and data transfer choices in order to contrast the advantages and disadvantages of employing each. The paper concludes with an example and elaboration of the tipping point where it becomes more expensive to store the output than re-running a set of simulations for a suficiently large prametric ensemble.