gdal VS Airflow

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.

Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.

In week Two, I contributed to the Apache Airflow repository.

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.

Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.

AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!

I agree there are many options in this space. Two others to consider:

- https://airflow.apache.org/

- https://github.com/spotify/luigi

There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…

You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte