Currently, there is lack of consideration of dependencies between data in big data processing, resulting in low data processing efficiency with large amounts of data transfer during task execution. In order to reduce data transfer and improve processing performance, this paper proposes a data-dependency driven task scheduling scheme, named D³S², for big data processing. D³S² is mainly composed of two parts:dependency-aware placement mechanism(DAPM), and transfer-aware task scheduling mechanism(TASM). DAPM discovers dependency between data so that strongly related data will be clustered and assigned to nodes in the same rack, thereby reducing the cross-rack data migration. TASM schedules tasks simultaneously after data placement according to the data locality constraint, so as to minimize the data transfer cost during the task execution. DAPM and TASM provide basis for decision making to each other, iterating constantly to adjust the scheduling scheme with the goal of minimizing the execution cost until an optimal solution is reached. The proposed scheme is verified in Hadoop environment. Experiments show that compared to native Hadoop, D³S² reduces the data transfer during job execution, and shortens job running time.