Improving the efficiency of Clusterix-like dbms for big data analytical processing

Commercial OLAP-systems are economically unavailable for organizations with limited financial capabilities. Analytical processing large amounts of data in these organizations can be accomplished using open source software systems on a cost-effective cluster platform. Previously created Clusterix-like DBMS were not efficient enough according to the «performance/cost» criterion. With a view to the enhance the effectiveness of such systems in the article considers their further development with a focus on a full load of processor cores and the using GPU acceleration (systems Clusterix-N, N – from New) up to the development of a system comparable in efficiency to the open source system Spark, which is currently considered the most promising. The development methodology was based on the constructive system modeling methodology.