  We address one of the important problems in Big Data, namely how to combine
estimators from different subsamples by robust fusion procedures, when we are
unable to deal with the whole sample. We propose a general framework based on
the classic idea of `divide and conquer'. In particular we address in some
detail the case of a multivariate location and scatter matrix, the covariance
operator for functional data, and clustering problems.
