dagdog

Lightweight DAGs for data analysis dev ops. By the time you finish a project with this lil puppy, you'll never want to use a Jupyter notebook again because

• you will have wasted less time waiting for your notebook to rerun, by iterating rapidly on modular components instead of rerunning your entire analysis after every code update.
• your code will already be closer to production-ready, as well as more understandable and reliable.

Note that this is NOT a replacement for tools like dagster or prefect. Those tools emphasize production and monitoring, while dagdog is strictly a dev-ops tool, allowing a user to rapidly sketch out an analysis pipeline without worrying about production considerations. This allows proofs-of-concept to grow faster (and fail faster).

Getting started

See the demo. In brief, here's how to work with dagdog:

• Structure your data analysis pipeline as a collection of python modules, with each module defining exactly one task.
• Within each module, implement the task using a method named __run__, with no arguments. (You might choose to call __run__ from under if __name__ == "main":, but dagdog does not care about that and will access __run__ directly.)
• Create a project entrypoint script, imitating demo/project.py to define the execution order of your tasks.
• Call your project entrypoint, dropping you into an interactive python session, where you can finally call any of the various execution and introspection methods on the dog DAG object.

If working directly on this repo, consider using the simplest-possible virtual environment.

Design goals

• Easiest-possible usage of a DAG to coordinate execution of a collection of tasks during data analysis dev ops.
• Flexible commands for execution of the DAG, including running a task in isolation, running only upstream tasks, or running only downstream tasks, etc.
• All configurations managed natively in python -- users don't need to mess with yaml or json files.
• Prioritize simplicity above feature-completeness.