This paper presents a state-of-the-art cloud-based system for academic research GPS data processing on a big scale. The framework tackles the major issues of cost-effectiveness, processing efficiency, and data volume, which are frequently obstacles to utilizing location-based insights. The suggested method makes use of a thorough Geo Data Warehouse strategy and makes use of Apache Spark, AWS services, and Apache Sedona. This integrated solution significantly reduces computing time and resource needs by streamlining the whole data lifecycle, from ingestion to analysis. This framework’s main contributions are its automated ETL orchestration, powerful post-processing phase designed for geographic analysis, and effective data-splitting algorithms. These developments have made large-scale location data available to researchers in a variety of fields and allowed for significant gains in cost savings, analytical power, and data handling efficiency. Best practices for putting such a framework into place and keeping it up to date in academic settings are also covered in the article. To guarantee the long-term acceptance and application of this framework entails attending to important issues like security, data governance, and knowledge transfer, improving research capacities, encouraging interdisciplinary cooperation, and democratizing access to sophisticated data analytic methods in higher education are the main objectives of this endeavor. This framework enables researchers to fully utilize location-based insights, spurring creativity and discovery in a variety of academic domains by tackling operational and technical obstacles.