TL;DR: GeoETL v0.6.0 adds ST_Area and ST_Length spatial UDFs for calculating polygon areas and line lengths/perimeters in SQL queries.

TL;DR: GeoETL v0.7.0 adds 29 new spatial SQL functions including predicates (ST_Intersects, ST_Contains), geometry generators (ST_Buffer, ST_ConvexHull), validators (ST_IsValid), and accessors (ST_X, ST_Y).

TL;DR: GeoETL v0.5.0 adds spatial UDFs (ST_Distance, ST_Point, ST_GeomFromText) powered by GEOS for spatial operations in SQL queries during conversion.

TL;DR: GeoETL v0.4.0 adds powerful SQL query support via the --sql flag, enabling filtering, column selection, aggregations, and transformations during format conversion—no intermediate files needed.

TL;DR: GeoETL v0.3.1 enhances developer experience with shell completions for 5 shells (bash, zsh, fish, powershell, elvish) and lays groundwork for 4 major geospatial formats coming soon.

GeoETL v0.3.0 adds full GeoParquet format support with production-ready performance.

Key highlights:

• ✅ Full read/write support
• ⚡ 3,315 MB/min processing throughput
• 💾 Streaming architecture with minimal memory
• 🚀 Handles 100M+ features efficiently

TL;DR: We built systematic benchmarking for GeoETL to understand performance across formats. Results: CSV is production-ready (2.3 GB/min, 50 MB RAM), while GeoJSON needs optimization (297 MB/min, 84 MB RAM). Benchmarking revealed a 7.6x performance gap and guides our optimization roadmap. Here's how we built the infrastructure and what we learned.

TL;DR: GeoETL v0.2.0 delivers on our performance promises with a production-ready streaming architecture. CSV processing achieves 2,266 MB/min throughput (7.6x faster than GeoJSON) while maintaining constant O(1) memory usage. Process 4.2 GB files in just 49.9 MB of memory.

In our previous posts, we introduced GeoETL and showed you how to perform your first data conversion. Now, let's take a deeper look under the hood and explore the architecture that makes GeoETL a powerful and high-performance geospatial ETL tool.

Welcome to your first GeoETL tutorial! In this post, we'll walk you through a simple data conversion task using the geoetl-cli. By the end of this tutorial, you'll be able to convert a GeoJSON file to a Parquet file.