Data processing computer systems store and process large volumes of data. The volumes tend to grow very quickly, especially in data warehouse systems. A few years ago data warehouses were used only for supporting strictly business decisions but nowadays they find their application in many domains of everyday life. New and very demanding field is stream data warehousing. Car traffic monitoring, cell phones tracking or utilities meters integrated reading systems generate stream data. In a stream data warehouse the ETL process is a continuous one. Stream data processing poses many new challenges to memory management and data processing algorithms. The most important aspects concern efficiency and scalability of the designed solutions. In this paper we present an example of a stream data warehouse and then, basing on the presented example and our previous work results, we discuss a solution for stream data parallel processing. We also show, how to integrate the presented solution with a spatial aggregating index.