The MapReduce programming model is widely acclaimed as a key solution to designing data-intensive applications. However, many of the computations that fit this model cannot be expressed as a single MapReduce execution, but require a more complex design. Such applications consisting of multiple jobs chained into a long-running execution are called pipeline MapReduce applications. Standard MapReduce frameworks are not optimized for the specific requirements of pipeline applications, yielding performance issues. In order to optimize the execution on pipelined MapReduce, we propose a mechanism for creating map tasks along the pipeline, as soon as their input data becomes available. We implemented our approach in the Hadoop MapReduce framework. The benefits of our dynamic task scheduling are twofold: reducing job-completion time and increasing cluster utilization by involving more resources in the computation. Experimental evaluation performed on the Grid'5000 testbed, shows that our approach delivers performance gains between 9% and 32%.