This research explores a comprehensive dataset extracted from the Google Play Store, encompassing over 10,000 mobile applications across various categories, types, and user engagement metrics. The primary objective of the study is to analyze and visualize key dimensions and measures—such as app category, rating, number of reviews, installation count, pricing models, and content types—in order to identify meaningful patterns and trends in user behavior, market distribution, and app performance. By structuring the dataset into dimensions and measures, and establishing the relationships between them, the research aims to reveal correlations such as how app category influences the number of installs, how pricing affects user ratings, and how review counts correspond to content quality. The visual representation of the data plays a central role in this analysis, transforming raw information into digestible insights through charts, graphs, and heatmaps. This not only enhances interpretability but also supports data-driven conclusions. The societal relevance of this study is significant, as it reflects the current digital consumption patterns, informs developers about user preferences, and contributes to better app development and deployment strategies. The study ultimately aspires to provide actionable insights for stakeholders within the app development ecosystem and offers a foundation for further research into user interaction trends in the mobile technology domain.