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In real-time data warehouses, data import is no longer implemented in the batched and periodic way during the idle time of data warehouses, but continuously ongoing. The updates of real-time data warehouses are conflict with queries against data warehouses. Thus the scheduling of updates and queries becomes a key issue. This paper proposes a priority-based balance scheduling algorithm (PBBS). Firstly, according to the response time requirements of queries and the different import levels of the data being updated, the algorithm gives different priorities to all tasks. Then it makes a parallel scheduling, considering the task priorities, the implementation conditions of task queues and the feedback of system resources. And it proposes a method that ensures data consistency for parallel tasks. Finally, the experiments show that the algorithm is not only able to adjust the resources allocation for updates and queries in accordance with user requirements, but also make rational use of system resources and ensure high-priority tasks are processed first. Thus it not only reduces the response time of the important queries, but enhances the data freshness of the important data.