Transform geospatial source data into cloud-native optimized formats using DuckDB as the core processing engine. High-performance batch processing for cloud deployment.
High-performance data preparation for cloud infrastructure
Unified processing using DuckDB with spatial and httpfs extensions for high-performance analytical queries.
Vector formats (GPKG, SHP, GeoJSON, FlatGeoBuf), LiDAR (LAS/LAZ), and Raster (GeoTIFF) in one pipeline.
Direct upload to AWS S3, Azure Blob Storage, or Google Cloud Storage with automatic bucket management.
Spatial sorting using Hilbert curves for optimized query performance on cloud-native formats.
Process entire directories with per-file error isolation. Continue processing even if individual files fail.
Command-line interface for automation, scripting, and integration with CI/CD pipelines.
Convert to cloud-optimized formats like FlatGeoBuf, COPC, and COG for efficient cloud access.
Real-time progress reporting with detailed logging for monitoring large batch operations.
Comprehensive format support for all your geospatial data
GeoPackage, Shapefile, GeoJSON, FlatGeoBuf, GeoParquet, and other OGR-supported formats.
FlatGeoBuf, GeoParquet, Cloud-Optimized GeoPackage with spatial indexing and sorting.
LAS/LAZ point cloud conversion to COPC (Cloud-Optimized Point Cloud) for streaming access.
GeoTIFF to COG (Cloud-Optimized GeoTIFF) with tiling, overviews, and internal compression.
AWS S3, Azure Blob Storage, Google Cloud Storage with presigned URLs and credential support.
Read remote files directly via HTTP/HTTPS using DuckDB httpfs extension for cloud datasets.
Optimized for cloud-native geospatial workflows
Improves spatial query performance by 10-100x for common bbox queries on large datasets.
Enable HTTP range requests for partial file access without downloading entire datasets.
Analytical query performance with columnar storage and parallel processing capabilities.
Transform your geospatial data into cloud-native formats optimized for cloud storage and query performance.
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