I am used to work with Argo which has capabilities to execute process steps in parallel, based on inputs from previous steps (e.g. see <https://dzone.com/articles/parallel-workflows-on-kubernetes>).

Let's use an easy example: a first step requests an API and receives a list of matching entries. The second will download and process the entries accordingly. The second step can be parallelized as the entries are independent from each other. In a third step, the results are stored somewhere, depending on all executions from step 2 (e.g. "item X succesfully procesed"). Classic divide & conquer, see below illustration.

[![enter image description here](https://i.stack.imgur.com/YNrne.png)](https://i.stack.imgur.com/YNrne.png)

I could not find documentation on how to do this in Airflow (2.0+). Is it possible, maybe with XComs?

Edit: One possible use cases would be satellite data processing. Consider e.g. the [Open Access Hub of Copernicus](https://scihub.copernicus.eu/), which provides an API to search for satellite scenes. I want to execute the processing of e.g. Sentintel-1 (radar data) in parallel for a given query. All scenes that match the query are relevant but do not depend on each other. Computation is heavy CPU + possibly GPU intensive, so spreading on workers makes sense in terms of overall performance.