Industrial big data integration and sharing (IBDIS) is of great significance in managing and providing data for big data analysis in manufacturing systems. A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks. First, a task flow graph (TFG) is designed to model the data analysis process. The TFG is composed of several tasks, which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy. Second, the function of Fog-IBDIS to enable data integration and sharing is presented in five modules: TFG management, compilation and running control, the data integration model, the basic algorithm library, and the management component. Finally, a case study is presented to illustrate the implementation of Fog-IBDIS, which ensures raw data security by deploying the analysis tasks executed by the data generators, and eases the network traffic load by greatly reducing the volume of transmitted data.