Research on cloud-based Big Data analytics has focused so far on optimizing the performance and cost-effectiveness of the computations, while largely neglecting an important as-pect: users need to upload massive datasets on clouds for their computations. This paper studies the problem of run-ning MapReduce applications when considering the simulta-neous optimization of performance and cost of both the data upload and its corresponding computation taken together. We analyze the feasibility of incremental MapReduce ap-proaches to advance the computation as much as possible during the data upload by using already transferred data to calculate intermediate results. Our key finding shows that overlapping the transfer time with as many incremental com-putations as possible is not always efficient: a better solution is to wait for enough to fill the computational capacity of the MapReduce cluster. Results show significant performance and cost reduction compared with state-of-the-art solutions that leverage incremental computations in a naive fashion.