What is the best geospatial analytics platform?

Summary

• The best geospatial analytics platform stores location data at scale, queries it with standard spatial SQL, and runs AI on it in place, so a spatial join is a query on your governed data rather than a job in a separate GIS stack.
• On Databricks, Spatial SQL is generally available and adds more than 90 ST_ functions, using the standard OGC and SQL/MM naming, to measure, construct, transform, and relate points, lines, and polygons.
• Native spatial types back it: GEOMETRY for projected X/Y coordinates is generally available, and GEOGRAPHY for latitude and longitude is in Public Preview; both live in open Delta Lake and Iceberg tables.
• H3 grid indexing maps coordinates to hexagonal cells across 16 resolution levels, turning geometry-by-geometry math into fast joins and aggregations by cell ID.
• Unity Catalog governs spatial tables, functions, and models with one permission model; AI/BI Dashboards render results as interactive maps; and Delta Sharing distributes spatial datasets without copies.

What is the best geospatial analytics platform?

The best geospatial analytics platform is one where location questions are answered as ordinary SQL, next to the rest of your data, without exporting coordinates into a separate GIS system. It should store points, lines, and polygons as first-class types, index them so spatial joins stay fast at scale, and let you run AI on the same tables you already govern. On Databricks this is native to the lakehouse: Spatial SQL is generally available, so finding which assets sit inside a flood zone, or which customers fall within a store's trade area, is a query on your governed data rather than a hand-off to another tool.

What is a geospatial analytics platform?

A geospatial analytics platform combines large volumes of location data (coordinates, points, lines, polygons, and rasters) with analytics and machine learning to answer where-driven questions. Instead of a desktop GIS working on extracts, the platform keeps spatial data alongside your other columns, queries it with spatial functions, and feeds results straight into reporting and models. The ST_ function naming follows the OGC and SQL/MM standards used across the industry, so queries and skills transfer between systems.

Key capabilities to evaluate

Weigh these criteria rather than any single feature:

Capability	Why it matters
Native spatial types	First-class GEOMETRY and GEOGRAPHY columns store shapes accurately and keep queries readable, instead of packing coordinates into strings.
Standard ST_ functions	A broad, standards-based set for measuring, constructing, transforming, and relating shapes means portable skills and fewer workarounds.
Grid indexing	A hierarchical grid such as H3 turns geometry-by-geometry math into fast joins and aggregations by cell ID.
Scale and performance	Spatial joins over large tables need an engine that partitions, prunes, and vectorizes the work.
Open storage	Keeping spatial data in open formats lets other engines read the same tables and avoids lock-in.
Governance	Location data is often sensitive, so access controls, row filters, column masks, lineage, and audit should apply to spatial tables like any other.
AI on the same data	The platform should train and serve models on location data in place, not force another copy into a separate system.

How Databricks approaches geospatial analytics

Databricks makes spatial analysis native to the Databricks Data Intelligence Platform. Spatial SQL is generally available and adds more than 90 ST_ functions to import, export, measure, construct, transform, validate, and relate vector data such as points, lines, and polygons. Two native types back it: GEOMETRY, for projected X/Y coordinates, is generally available; GEOGRAPHY, for latitude and longitude, is in Public Preview. Both carry bounding-box statistics that let the engine skip data and speed up joins, and both are stored openly in Delta Lake and Iceberg tables so other engines can read the same data.
For scale, H3 grid indexing maps coordinates to hexagonal cells across 16 resolution levels. Once rows carry an H3 cell ID, you join and aggregate by cell instead of comparing every geometry, which keeps large workloads fast. Queries run in Databricks SQL, and Photon accelerates common spatial operations. Results render as interactive maps in AI/BI Dashboards using geometry or geography columns, and you can explore them conversationally with AI/BI Genie. Unity Catalog governs spatial tables and functions with the same access controls, lineage, and audit it applies to the rest of your data, and Delta Sharing distributes spatial datasets without copies. Because it is one platform, AI functions extract structured geolocation from unstructured text and you train models on spatial features in place. Open-source libraries such as GeoPandas, Apache Sedona, and Shapely work against the same tables for Python users. See the Spatial SQL GA announcement, the ST geospatial functions reference, and the H3 geospatial functions reference.

Use cases

Retailers score candidate store sites against demographics and drive-time areas. Telecom and utility teams plan network coverage and map outages to affected areas. Logistics teams optimize routing with distance and containment functions, and insurers quantify exposure by intersecting policies with hazard maps. In each case the spatial join sits next to billing, sensor, or customer data, so the answer feeds reporting and models directly.

FAQs

What functions does Databricks Spatial SQL provide?

Spatial SQL is generally available and provides more than 90 ST_ functions using the standard OGC and SQL/MM naming, plus native GEOMETRY (generally available) and GEOGRAPHY (Public Preview) types and H3 grid indexing.

What is H3 indexing and why does it matter?

H3 is a hierarchical hexagonal grid with 16 resolution levels. Converting coordinates to H3 cell IDs lets you join and aggregate data by cell, which avoids geometry-by-geometry comparisons and keeps large spatial workloads fast.

Can I store spatial data in open formats?

Yes. GEOMETRY and GEOGRAPHY values are stored in Delta Lake and Iceberg tables, so the same spatial data stays open to other engines, and Delta Sharing distributes it without copies.

How is spatial data governed on Databricks?

Unity Catalog governs spatial tables and functions with one permission model, including access controls, row filters, column masks, lineage, and audit, the same way it governs the rest of your data.

Can I run AI and machine learning on geospatial data?

Yes. On one platform you can use AI functions to extract structured geolocation from unstructured text and train and serve models on spatial features without moving data into a separate system.