Hadoop MapReduce framework is an important distributed processing model for large-scale data intensive applications. The current Hadoop and the existing Hadoop distributed file system’s rack-aware data placement strategy in MapReduce in the homogeneous Hadoop cluster assume that each node in a cluster has the same computing capacity and a same workload is assigned to each node. Default Hadoop doesn’t consider load state of each node in distribution input data blocks, which may cause inappropriate overhead and reduce Hadoop performance, but in practice, such data placement policy can noticeably reduce MapReduce performance and may increase extra energy dissipation in heterogeneous environments. This paper proposes a resource aware adaptive dynamic data placement algorithm (ADDP).With ADDP algorithm, we can resolve the unbalanced node workload problem based on node load status. The proposed method can dynamically adapt and balance data stored on each node based on node load status in a heterogeneous Hadoop cluster. Experimental results show that data transfer overhead decreases in comparison with DDP and traditional Hadoop algorithms. Moreover, the proposed method can decrease the execution time and improve the system’s throughput by increasing resource utilization.