Dynamic Factor Models, which assume the existence of a small number of unobserved latent factors that capture the comovements in a system of variables, are the main big data tool used by empirical macroeconomists during the last 30 years. One important tool to extract the factors is based on Kalman lter and smoothing procedures that can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity and many other characteristics often observed in real systems of economic variables. This paper surveys the literature on latent common factors extracted using Kalman lter and smoothing procedures in the context of Dynamic Factor Models. Signal extraction and parameter estimation issues are separately analyzed. Identication issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases.