Pre-computed data cube facilitates OLAP (On-Line Analytical Processing). It is a well-known fact that data cube computation is an expensive operation, which attracts a lot of attention. While most proposed algorithms devoted themselves to optimizing memory management and reducing computation costs, less work addresses one of the fundamental issues: the size of a data cube is huge when a large base relation with a large number of attributes is involved. In this paper, we propose a new concept, called a condensed data cube. The condensed cube is of much smaller size of a complete non-condensed cube. More importantly, it is a fully pre-computed cube without compression, and, hence, it requires neither decompression nor further aggregation when answering queries. Several algorithms for computing condensed cube are proposed. Results of experiments on the effectiveness of condensed data cube are presented, using both synthetic and real-world data . The results indicate that the proposed condensed cube can reduce both the cube size and therefore its computation time.