In this paper, we propose a sound approach and an algorithm ¹ for computing a condensed representation of either full or iceberg datacubes. A novel characterization of datacubes based on dimensional-measurable partitions is introduced. From such partitions, iceberg cuboids are achieved by using constrained product linearly in the number of tuples. Moreover, our datacube characterization provides a loss-less condensed representation specially suitable when considering the storage explosion problem and the I/O cost. We show that our algorithm Ccube turns out to an operational solution more efficient than competive proposals. It enforces a lecticwise and recursive traverse of the dimension set lattice and takes into account the critical problem of memory limitation. Our experimental results shows that Ccube is a promising candidate for scalable computation.