In today's rapidly evolving manufacturing processes, the digital twin is revolutionizing industrial processes through real-time data-driven insights. Resistance Spot Welding (RSW), a commonly used joining technique in the automotive and aerospace industries, is a key area where digital twin technology holds great promises for improving quality, efficiency, and predictive maintenance. However, the integration of digital twin models with RSW requires robust data management systems, capable of handling large volumes of data. This thesis presents the design and implementation of a data management system (DMS) that enables the development and operation of a digital twin for Resistance Spot Welding. The proposed DMS addresses the critical challenges in data collection, processing, storage, and analysis by incorporating modern data architecture principles with advanced analytics.