A logistics team stores 40 million delivery stops in an Apache Iceberg table. Every row has a latitude and a longitude. The analyst wants every stop inside a polygon that outlines one metro area. The query engine scans every data file in the table, because nothing in the table metadata tells it which files contain points inside that polygon. Forty million rows get read to return two hundred thousand.
That was the normal state of spatial data on the lakehouse for most of a decade. Coordinates lived in two double columns or in an opaque binary column. The table format did not know the column was spatial. The file format did not know either. Every optimization that Iceberg applies to timestamps, integers, and strings, from min/max pruning to partition transforms, simply did not apply.
Iceberg format version 3 changes this by adding two native primitive types: geometry and geography. Apache Parquet 2.11 added matching logical types at the file level. Together they give spatial data the same standing as any other column: a declared type, a coordinate reference system that travels with the schema, and per-file bounding-box statistics that let an engine skip files before reading a single shape.
