Geospatial data processing can be challenging, especially when dealing with large datasets. While PostgreSQL and PostGIS are popular choices for geospatial analysis, they may not perform well on huge amounts of data. In contrast, databases like CitusDB, Greenplum, Amazon Redshift, MapD, ClickHouse, Vertica, and Druiddb offer better performance but often with limited support for geospatial functionality. The author tested ClickHouse with the NYC Taxi dataset and found impressive results, achieving a 10x improvement in query performance by creating a new table with a quadkey column as an index. Quadkey is a way to encode lat-lon pairs into integers, allowing for efficient querying of large datasets.