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The process of designing a parallel data warehouse has two main steps: (1) fragmentation and (2) allocation of generated fragments at various nodes. Usually, fragmentation and allocation tasks are used iteratively (we first split the warehouse horizontally and then allocate fragments over the nodes). The main drawback of such design approach (called iterative) is that it does not take into account the interdependencies between fragmentation and allocation since the generated fragments are the input of data allocation problem. In this paper, we consider a parallel data warehouse design approach combining data fragmentation and allocation. Its main characteristic is that it decides on the quality of the allocation schema when fragmenting the warehouse. Our approach is validated using computational tests over a variety of parameter values.