Query billions of rows in seconds. Process terabytes of Parquet data. Run complex spatial analysis locally without a database server. This is the future of geospatial analytics.
The analytical database engine built for modern data workloads
Extend capabilities with specialized extensions
Full geospatial support with GEOS-powered geometry operations and spatial indexing.
Query data directly from cloud storage without downloading entire files.
Query Apache Iceberg tables for data lakehouse analytics.
Read Delta Lake tables with ACID transactions and versioning.
Fast text search with inverted indexes and relevance scoring.
Connect to external databases and query across sources.
Use familiar SQL syntax with powerful geospatial extensions
-- Find all buildings within 1km of fire stations -- Query runs on 50M+ rows in under 2 seconds SELECT b.building_id, b.address, ST_Distance(b.geom, f.geom) AS distance_meters, f.station_name FROM read_parquet('s3://data/buildings/*.parquet') b JOIN fire_stations f ON ST_DWithin(b.geom, f.geom, 1000) WHERE b.building_type = 'residential' AND b.year_built < 1970 ORDER BY distance_meters;
Built for serious geospatial analysis
Process datasets larger than memory with streaming window functions and aggregations.
Optimized join algorithms including spatial joins with R-tree indexes.
Full SQL window function support for running totals, rankings, and moving averages.
Push filters to the data source to minimize I/O and accelerate queries.
Access data wherever it lives without moving it first
Query Parquet, CSV, and JSON files directly from S3 buckets
Access Azure Data Lake and Blob Storage containers
Query data in Google Cloud Storage buckets
Access any file via URL with range request support
Automatic partition discovery and pruning
IAM roles, access keys, and managed identities
Benchmarks on typical geospatial workloads
Benchmarks on laptop with Intel i7, 32GB RAM, NVMe SSD
Work directly with modern and legacy formats
From raw data to visualization in one platform
Import from files, databases, or cloud storage
Interactive query editor with autocomplete
Save and rerun previous analyses
Browse, sort, and filter query results
Display spatial results on interactive maps
Save to Parquet, GeoPackage, or other formats
Unlock insights from massive geospatial datasets
Analyze billions of location events to understand movement patterns, dwell times, and customer behavior.
Overlay property data with hazard zones, flood maps, and demographic data for insurance and planning.
Query and analyze millions of infrastructure assets with spatial relationships and maintenance history.
Process satellite-derived datasets, climate data, and environmental monitoring at scale.
Optimize logistics with spatial analysis of warehouses, routes, and delivery zones.
Property valuations, market analysis, and site selection using parcel and transaction data.
DuckDB vs traditional approaches
| Capability | DuckDB (GeoAnalytics Pro) | PostGIS | Spark/Sedona |
|---|---|---|---|
| Server Required | No | Yes | Yes (cluster) |
| Setup Complexity | Zero config | Moderate | High |
| Query Parquet Directly | Yes | No | Yes |
| Offline Capable | 100% | Yes | No |
| Analytics Performance | Excellent | Good | Excellent |
| Spatial Functions | PostGIS Compatible | Full | Limited |
| Desktop Integration | Native | Via network | Via network |
See how DuckDB-powered analytics transforms your geospatial workflows.
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