This talk focuses on assessing climate change-induced thermal shifts in the Republic of Kosovo using statistical models and open-source data. It employs the Mann-Kendell and Sens Slope statistical methods to analyze completed MODIS LST mission data. The study aims to understand land surface temperature shifts during day and night over different time periods, providing insights into the current and projected future impacts of climate change on tourist economies in Kosovo. Additionally, water balance analysis is used to evaluate climate change's effects on wine grape production and identify regions vulnerable to flood-related hazards. The findings will help develop resilience strategies for the affected areas. Furthermore, this presentation introduces a novel framework for time-series analysis of big data to assess climate change impacts on developing economies, with a focus on tourism. It utilizes open datasets and analyzes the temperature changes in data-scarce environments. The study's results are expected to be valuable for governments, municipalities, and NGOs, providing insights into how climate change will affect their communities and helping inform climate adaptation strategies. It discusses the use of Google Earth Engine for modeling remote sensing data, emphasizing the importance of open-source tools in climate intelligence building, particularly in regions with limited resources. The Republic of Kosovo's efforts to develop tourism-dependent economic sectors, such as the wine region of Rahovec and Prizren, are at risk due to climate change. This project aims to showcase the potential of open big data analysis and open-source learning tools to provide essential insights into climate change impacts in resource-constrained countries. It highlights the significance of developing open-data models to inform climate adaptation strategies in regions facing climate-related challenges.