Execution plans produced by traditional query optimizers for data integration queries may yield poor performance for several reasons. The cost estimates may be inaccurate, the memory available at run-time may be insufficient, or data delivery rate can be unpredictable. In this paper, we address the problem of unpredictable data arrival rate. We propose to dynamically schedule queries in order to deal with irregular data delivery rate and gracefully adapt to the available memory. Our approach performs careful step-by-step scheduling of several query fragments and processes these fragments based on data arrivals. We describe a performance evaluation that shows important performance gains in several configurations.