Convert coordinates to H3 cells, traverse the hexagonal grid, and run spatial aggregations directly in SQL. H3 min/max statistics let the query planner skip irrelevant Parquet files on large tables.
Indexing, traversal, and spatial predicates without leaving the query
Convert coordinates into H3 cells, then group, count, and run H3 spatial joins on Delta tables by matching datasets on a shared cell. Sixteen resolutions span continent-scale buckets down to sub-meter precision.
Find neighbors, draw rings and disks, walk paths between cells, and move up or down the resolution hierarchy. Hex topology is built in, not bolted on.
PostGIS-style functions for distance, containment, area, centroid, and buffer. Existing spatial SQL ports across with minimal change.
Spatial queries on large tables only read the files that matter
Each Parquet file records the H3 cell range of its rows. Spatial predicates let the planner drop any file whose range does not intersect the query region.
Cell equality, parent and child containment, and distance filters. Composes with Delta partitioning and Z-ordering on the same table.
H3 cell analytics over point data, stated plainly
The sweet spot is point-heavy workloads: bucket events into cells, join datasets on a shared H3 cell, and roll results up or down the resolution hierarchy on Delta tables.
DeltaForge does not implement geometry column types or GeoParquet. If your workload centers on polygon editing or topology operations, a dedicated GIS such as PostGIS remains the right tool.
Hands-on guides for the Delta tables your H3 queries run on
Keep the tables behind your spatial queries compact and well clustered, including the Z-ordering that H3 pruning composes with.
Load raw point data from CSV into a Delta table you can index with H3, using nothing but SQL.
H3 indexing and spatial functions built into the SQL engine.