The eBird dataset, which contains over 1.5 billion bird observations worldwide, was analyzed using ClickHouse, a column-store database management system. The data was loaded into ClickHouse using the `clickhouse-local` tool and then transformed to optimize its performance in a web-based application. The dataset was preprocessed by analyzing its structure, creating a table schema, and applying dictionary encoding to reduce storage requirements. The optimized table structure included materialized columns for efficient mapping applications, and indices were created for faster search capabilities. The data was loaded into ClickHouse using the `clickhouse-client` tool and resulted in a significant reduction in storage size compared to the original zip file. The database management system's ability to handle large-scale geographical datasets made it an ideal choice for this project.