To address the new challenges that big data has brought on data storage, management and analysis, distributed file systems and MapReduce programming model have been widely adopted in both industry and academia. This paper proposes a distributed MOLAP technique, named DOLAP (distributed OLAP), based on Hadoop distributed file system (HDFS) and MapReduce program model. DOLAP adopts the specified multidimensional model to map the dimensions and the measures. It comprises the dimension coding and traverse algorithm to achieve the roll up operation on dimension hierarchy, the partition and linearization algorithm to store dimensions and measures, the chunk selection strategy to optimize OLAP performance, and MapReduce to execute OLAP. In addition, the paper describes the application case of the scientific data analysis and compares DOLAP performance with other dominate non-relational data management systems. Experimental results show that huge dominance in OLAP performance of the DOLAP technique over an acceptable performance lose in data loading.