There has been a gradual shift from traditional computing to technologies involving more data-centric architectures. This has led to tremendous growth in the amount of data and, furthermore, the data-intensive applications. The major bottleneck with such applications is the data movement that is required between memory/storage and CPU. To improve the performance of such applications and reduce the movement of data from memory to CPU for computation, near data processing (NDP) is the alternative, which moves the computation from CPU to memory. This helps to improve performance, utilize bandwidth properly and reduce energy consumption to a great extent, which helps to execute data-intensive applications efficiently. In this paper, we have discussed the basics of NDP and the methods which have been proposed for the implementation of NDP. We have also discussed the applications that require NDP for better performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.