The rapidly increasing demand and complexity of manufacturing process potentiates the usage of manufacturing data with the highest priority to achieve precise analyze and control, rather than using simplified physical models and human expertise. In the era of data-driven manufacturing, the explosion of data amount revolutionized how data is collected and analyzed. This paper overviews the advance of technologies developed for in-process manufacturing data collection and analysis. It can be concluded that groundbreaking sensoring technology to facilitate direct measurement is one important leading trend for advanced data collection, due to the complexity and uncertainty during indirect measurement. On the other hand, physical model-based data analysis contains inevitable simplifications and sometimes ill-posed solutions due to the limited capacity of describing complex manufacturing process. Machine learning, especially deep learning approach has great potential for making better decisions to automate the process when fed with abundant data, while trending data-driven manufacturing approaches succeeded by using limited data to achieve similar or even better decisions. And these trends can demonstrated be by analyzing some typical applications of manufacturing process.