To ensure continuity of space weather prediction capabilities and improve understanding of space weather phenomena, the National Oceanic and Atmospheric Administration (NOAA) has been increasing its portfolio of spaceborne instruments in recent years and plans to continue to do so in the coming years. Since 2016, the Solar Ultraviolet Imager (SUVI), a 6-channel instrument, has flown on three Geostationary Operational Environmental Satellite Network (GOES) missions and is planned to launch again in 2024 as part of the GOES-U mission. SUVI features a robust data processing pipeline that consistently provides imaging data of multi-thermal coronal structures and their dynamics in the low corona. Looking ahead and anticipating the decommissioning of the SOHO/LASCO coronagraph, NOAA is set to launch two Compact Coronagraph (CCOR) instruments that will capture unpolarized white light. For the first time one of the coronagraphs will fly into geosynchronous orbit as part of the 2024 GOES-U mission. The second one will be flown onboard the Space Weather Follow On Lagrange-1 (SWFO-L1) mission in 2025. Ensuring continuity of white light coronagraph observations is critical to NOAA's mission for forecasting coronal mass ejection (CMEs) arrival times. In this presentation, we will explore how Python classes are utilized in the development of the Ground Processing Algorithms (GPAs) for these instruments. Additionally, we will discuss the upcoming transition to a cloud-based system for running the GPAs, highlighting both the new opportunities for data reprocessing and the challenges in pipeline architecture.