In this talk, I will briefly recall the historical Kalman filter and its ensemble form. Then I will show how the latter has been successfully implemented for data assimilation, in particular in numerical weather forecasting. More recently, the Ensemble Kalman Filter has been proposed as a methodology for solving very general inverse problems in high-dimensional contexts. I will present the theory, show some simple applications and point out the numerous open problems that remain.