gdal VS Dask

TiTiler is a dynamic tile server built on FastAPI and Rasterio/GDAL. Its main features include support for Cloud Optimized GeoTIFF(COG), multiple projection methods, various output formats (JPEG, JP2, PNG, WEBP, GTIFF, NumpyTile), WMTS, and virtual mosaic. It also provides Lambda and ECS deployment environments using AWS CDK.

GDAL is the real heart, the python aspect is mostly wrappers around that I'm fairly sure. I love python for the record, the only reason I bring it up, is cause python haters accuse it of being slow, but QGIS drops down to C++ when speed is necessary, like most modern packages do.

gdal 0.15 (repo, docs), a set of Rust bindings for the GDAL library, used for access to geo-spatial data formats, is now out!

GDAL (Geospatial Data Abstraction Library) is a the geospatial data processing library. It handles a lot of Raster/Vector analysis and alteration. gdal_contour and gdal_rasterize which would be used to create isolines (contour lines) . There's more complex processing and analyses than that. More common is reprojecting multiple layers, and some that being as Vector files, into different coordiatne systems.

Access: GDAL

As an example we use GDAL heavily through its C# binds. We do all the additional data processing, that isn't done by the C++ GDAL, in C#. The final results are both Data (held in memory or temp exported to a file), and a normalized Raster Texture that we can display on a TextureRect. Most of the C# data processing scripts aren't even Inheriting from any Godot Class.

I think this would be the best way to handle this. GDAL is what you should look into for this project.

Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.

Dask

We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.

I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.

Dask: Distributed data frames, machine learning and more

To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:

Look into Dask, see: https://dask.org/

[1] https://dask.org/