1/14/2024 0 Comments Python prefect vs airflowCelery would fit nicely as another execution backend. Currently, Prefect ships with local, multiprocessing, threaded, and Dask distributed backends, and there’s an outstanding PR for a redis-queue backend. If all of that workflow logic “passes”, Prefect then runs your task on whatever backend you prefer, which is where Celery could come in to play. Prefect’s primary job is to supply workflow semantics around your functions - things like what to do if they fail, how to handle logging, retries, conditional logic, responding to upstream results, complex dependencies, scheduling, etc. Prefect, in contrast, is a workflow management system. Dask isn’t exactly the same but similar enough for the purpose of this conversation. Airflow is great for scheduling and monitoring workflow. So Celery’s primary job is to take a function and run it. Prefect: Excellent makes use of purposeful stream a Python-based API. A “broker” submits work to a pool of workers, who run the task/job/function and indicate that they’ve finished. Celery is a system for executing work, usually in a distributed fashion. Its modular design allows users to easily. Airflows key features include a user-friendly web interface, multi-node execution, and extensible architecture. It allows data engineers to programmatically author, schedule, and monitor workflows. Jeremiah: Hi, it’s a good question - many technologies can be used together, and in fact it would be straightforward to run Prefect on top of a Celery backend (though we prefer Dask for the same purpose). Airflow is an open-source data orchestration platform developed by the team at Airbnb. I think the differences from Airflow are well described in the docs, yet quite clear, but I'm not sure why I should prefer it over Celery. Archived from the Prefect Public Slack Communityįrassetto.stefano: This will probably sounds like a weird question, but how is Prefect different from Celery?.
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