The concept changes in continuously evolving data streams are termed as concept drifts. It is required to address the problems caused due to concept drift and adapt according to the concept changes. This can be achieved by designing supervised or unsupervised techniques in such a way, that concept changes are considered, and useful knowledge is extracted. This paper discusses various techniques to manage concept drifts. The synthetic and real datasets with different concept drifts and the applications are discussed.