This article presents a comprehensive analysis of developing and implementing an ETL (Extract, Transform, Load) pipeline for processing school data from OpenStreetMap (OSM) across multiple countries. The project's primary goal was to create a reliable database of educational institutions that could serve various applications, from educational planning to infrastructure development. The implementation faced significant challenges, including varying data quality across regions, multilingual content, and inconsistent classification systems. The solution involved a sophisticated three-phase approach: extraction using the OSM Overpass API, transformation with robust cleaning and validation processes, and loading into a standardized database format. The technical implementation proved particularly interesting in its handling of complex data scenarios. The system successfully processed school data for multiple countries, including Albania and Ukraine, implementing intelligent solutions for duplicate detection, education level classification, and multilingual data handling. Key achievements included the development of a sophisticated validation system, efficient handling of different OSM element types (nodes, ways, and relations), and the creation of a standardized data structure that maintained data quality while accommodating regional variations. The project demonstrated the potential of leveraging crowd-sourced data for educational planning while highlighting the importance of careful data processing and validation. The lessons learned and solutions developed provide valuable insights for similar projects working with geographical and educational infrastructure data.