What our tool does is allow users to organize the flow of their processing jobs on an infinite 2D layout, have some jobs run at the beginning of the flow while they organize another part to run later.
Unfortunately it's a big pile of messy code that depends too much on other "internal" systems so we can't open source it... I'd like to add "yet" because I try to gradually clean it up, simplify and make it more generic, but I'm not sure I'll see that day myself.
In the meantime... maybe Node-RED ? https://nodered.org/
I imagine I'll eventually need to add some sort of management system to move these dynamic jobs in and out of Airflow to keep them from bloating the database or cluttering the UI.
Cookiecutter takes a Python code file as input with Jinja interspersed (the template). When you want to make a new instance from the template, it gives command-line prompts, and then evaluates the Jinja logic — custom variables, loops, etc to output Python code. It took me 15–30 minutes to get started and has already paid off.
This allows to build ETL jobs which react to all sorts of external application triggers (such as an upload made by a user in that case, but it could be an API notification / webhook etc).
Based on your example, I would have a single dag that would 1. get user data and 2. generate a graph.
All the flexibility should be defined in whatever function, script or program you define to generate the graph.
Now. Airflow allows you to do what you're describing as well and will explain how to. If you were my coworker I'd dig deeper and try to understand whether the design you want is the design that is best, but let's assume it is. So first we support "externally triggered DAGs", which means those workflows don't run on a schedule, they run when they are triggered, either by some sensor, or externally in some way. A use case for that would be some company processing genomes files, and everytime a new genome file shows up, we want to run a static DAG for it. https://airflow.incubator.apache.org/scheduler.html#external...
We also support branching, meaning you can take different paths down the DAG based on what happened upstream. https://airflow.incubator.apache.org/concepts.html#branching
Now if your DAG's shape changes dramatically at every run [a shapeshifting DAG!], I would argue that conceptually they are different DAGs, and would instruct to build "singleton" DAGs dynamically. Meaning you have python code that creates a dag object [with its own dag_id] for each "instance", with the schedule_interval='@once', meaning each DAG will run only once. You can shape each DAG individually, from that same script, and craft whatever dependency you might like for each one.
Though all of this is not only possible and easy-ish to do, it may not be the best approach. Try to think of your DAGs and tables as static [or slowly changing] if you can, and the data as the variable.
As an analogy, try to think of an oil pipeline that changes shape based on the quality of the oil it processes. Crazy?! It's easier to think of the pipeline as static and infrastructure, and to have components that can sort and direct the flow in [existing and static] pipes.
Shaping DAGs dynamically poses a challenge to the scheduler on how to 'predict' what tasks need to run in the future. The scheduler needs to evaluate which tasks will need to run, without actually executing these tasks themselves. For Airflow in its current state that is a chicken and egg problem.
For the future, I can think of allowing dynamic dags being described through the Rest API, but that is definitely further out and has not really popped up yet on the horizon.
By dynamic, I mean something like "user sent us some new data to process, create a custom graph just for this data". I can create new airflow graph per each processing pipeline with new dag id every time, but airflow was not created for use case like this and it's not working well in such scenario.