In this article, we present and describe FRED-QD, a large, quarterly frequency macroeconomic database that is currently available and regularly updated at https://research.stlouisfed.org/econ/mccracken/fred-databases/. The data provided are closely modeled to that used in Stock and Watson (2012a). As in our previous work on FRED-MD (McCracken and Ng, 2016), which is at a monthly frequency, our goal is simply to provide a publicly available source of macroeconomic “big data” that is updated in real time using the FRED® data service. We show that factors extracted from the FRED-QD dataset exhibit similar behavior to those extracted from the original Stock and Watson dataset. The dominant factors are shown to be insensitive to outliers, but outliers do affect the relative influence of the series, as indicated by leverage scores. We then investigate the role unit root tests play in the choice of transformation codes, with an emphasis on identifying instances in which the unit root-based codes differ from those already used in the literature. Finally, we show that factors extracted from our dataset are useful for forecasting a range of macroeconomic series and that the choice of transformation codes can contribute substantially to the accuracy of these forecasts.