In this paper, a brief overview on data assimilation is provided in the context of oceanographic application. The ocean data assimilation needs to ingest various types of data such as satellites and floats, thus essentially requires dynamically-consistent assimilation methods. For such purpose, sequential and variational approaches are discussed and compared. The major advantage of the Kalman filter (KF) is that it can forecast error covariances at each time step. However, for models with very large dimension of state vector, the KF Is exceedingly expensive and computationally less efficient than four-dimensional variational assimilation (4D-Var). For operational application, simplified 4D-Var schemes as well as ensemble KF may be considered.