Cloud-Native Geospatial

Foundational concepts and shifting paradigms.Go

Metadata standards for discovery.Go

Efficient raster layouts for remote access.Go

Multi-dimensional datasets for the cloud using the Zarr v3 specification and sharding.Go

GeoParquet, FlatGeobuf, and more.Go

Transactional tables and native geospatial support in Iceberg v3.Go

Multi-dimensional analysis concepts using xarray and stackstac.Go

Egress, buckets, and performance tiers.Go

Distributed geospatial analysis at scale using Dask and local clusters.Go

Make old data formats cloud-ready: PyOgrio, GDAL, and the local-cache pattern.Go

Core skills for spatial engineering in Python.Go

Geometry types, spatial predicates, R-tree indexing, and multi-format I/O.Go

Monitoring geospatial data pipelines and spatial quality SLIs.Go

Cloud-native databases.Go

Creating discoverable spatial registries.Go

Global map hosting with zero servers.Go

Running geospatial compute client-side.Go

Large-scale interactive map rendering.Go

High-dimensional scientific analysis.Go

Efficient global analysis with hexagons.Go

Integrated global geospatial knowledge.Go

High-level design of a Chat-to-Map application.Go

Ingesting and cleaning data for the application.Go

Retrieval Augmented Generation for Geo-AI.Go

Building the user-facing spatial interface.Go

Optimizing metadata for LLM retrieval.Go

LLM-orchestrated spatial operations.Go

Building a CARTO-style agentic workbench.Go

Building a natural language interface for styling maps.Go

REST API, JS SDK embedding, and building a custom AI Extension.Go

Foundational concepts of applying machine learning.Go

The foundation for Earth AI.Go

Extracting features from high-res imagery using VLMs.Go

Benchmarking how well LLMs know spatial geometry.Go

Future challenges in cloud-native and AI.Go

Reviewing standard platforms and tools.Go