The author recently organized a data visualization hackathon at their company's all-company offsite event. The dataset used for the challenge was Foursquare Places, which contains information about places on a map, including shops, restaurants, parks, playgrounds, and monuments. The author created a ClickHouse database to handle the dataset and built a visualization tool using a small command-line tool that provides a full ClickHouse engine. The visualization tool is able to load the data in 42 seconds and take up 11 GB of space, and it provides a beautiful and interactive experience for users. The author compared their tool with other similar projects, including Foursquare Studio, which loads much slower and has a low resolution, and Kepler.gl, which is limited by its ability to process large datasets without lagging. Overall, the author concludes that ClickHouse is a good option for analytics on large-scale geographical datasets due to its fast query processing capabilities and scalability.