Contrails, formed under specific atmospheric conditions, have a noteworthy role in heat-trapping within the atmosphere. This study bridges the gap between theoretical contrail formation models and real-world data by employing flight information from OpenSky and meteorological data from the European Centre for Medium-Range Weather Forecasts. We introduce a computationally efficient contrail estimation module, leveraging a client-server architecture that allows on-demand weather data interpolation via an API, significantly reducing computational load and enhancing performance locally. The study also benchmarks the entire pipeline, from data acquisition to contrail prediction, offering a robust tool for future air traffic studies requiring interpolated weather data.