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Dabbling with Dagster vs. Airflow

davidsj.substack.com

11–20 of 56 posts

Re: Dabbling with Dagster vs. Airflow

#11

Where is a service that doesn't focus specifically on 1.) data pipelines / data-science 2.) cicd / build pipelines 3.) ... you name it I mean, just a service that gives me all the groundwork to build one of the above myself. Is there something like that?

Argo Workflows

Re: Dabbling with Dagster vs. Airflow

#12
The author seems to think that Dagster or Prefect will take over Airflow, I don't think this is true. All of them being open source means that if one has a good idea or better way of doing something the other can quickly implement the feature and even use the some of same code. We saw it with Airflow implementing the TaskFlow API as a response to Dagster, and in a few weeks Airflow 2.4 is going to have dataset scheduling released. So, Airflow's head start is going to be extremely hard to over come, if they remain adaptable.

Also, as other's have mentioned the comparison to MWAA is unfair, the true own of Airflow is Astronomer as they have over 50% of the commits to Airflow, and Astronomer is a much better product the MWAA.

Re: Dabbling with Dagster vs. Airflow

#14

Where is a service that doesn't focus specifically on 1.) data pipelines / data-science 2.) cicd / build pipelines 3.) ... you name it I mean, just a service that gives me all the groundwork to build one of the above myself. Is there something like that?

Argo Workflows

More like a SaaS that I can use without setting up my own infrastructure. I guess one could turn Argo Workflows into such an SaaS.

Re: Dabbling with Dagster vs. Airflow

#15

The author seems to think that Dagster or Prefect will take over Airflow, I don't think this is true. All of them being open source means that if one has a good idea or better way of doing something the other can quickly implement the feature and even use the some of same code. We saw it with Airflow implementing the TaskFlow API as a response to Dagster, and in a few weeks Airflow 2.4 is going to have dataset schedu…

For sure, between these competing products you will see one respond to a popular feature by mimicking it. But the Dagster framework is fundamentally different from Airflow so over time I don't see it being a feature-vs-feature decision but more of a declarative/reconciliation vs. imperative/task-centric split.

Re: Dabbling with Dagster vs. Airflow

#16
post #3

sounds like a very positive experience all around, but not going too deep into dagster itself yet. i feel like comparing MWAA to Dagster is not an even footing, i'd be interested in seeing how Astronomer has also improved the Airflow experience.

Maybe we will find out in David's second installment ??

Re: Dabbling with Dagster vs. Airflow

#17

The author seems to think that Dagster or Prefect will take over Airflow, I don't think this is true. All of them being open source means that if one has a good idea or better way of doing something the other can quickly implement the feature and even use the some of same code. We saw it with Airflow implementing the TaskFlow API as a response to Dagster, and in a few weeks Airflow 2.4 is going to have dataset schedu…

Is open source really a big factor? There's no magic sauce in the implementation in this kind of stuff, and you can't just copy and paste code over.

Re: Dabbling with Dagster vs. Airflow

#18

Where is a service that doesn't focus specifically on 1.) data pipelines / data-science 2.) cicd / build pipelines 3.) ... you name it I mean, just a service that gives me all the groundwork to build one of the above myself. Is there something like that?

AWS Simple Workflows or Azure Logic Apps are both services that let you define S2S workflows however you like without any particular bias to CI/CD or business operations.

If you want to go even lower level, a framework like DTFx lets you define long-running, distributed and resilient orchestrations in code:

https://github.com/Azure/durabletask

Re: Dabbling with Dagster vs. Airflow

#19
I recently had the experience of setting up some Prefect pipelines, which I can compare to this article. Note that while I'm not new to data engineering, I'm new to open source frameworks, and have some insight into Airflow (studied architecture in depth, written a lot of code in it).

Prefect is generally very easy to use. Essentially, you: (a) write a Python-based flow, which defines some job to run (with subtasks), (b) turn on an orchestrator on a server somewhere, (c) turn on an agent on a server somewhere (to run the flow when instructed by the orchestrator), and (d) connect to the orchestrator, build & apply a deployment, and run it.

I find the docs a little half baked right now. One example is that cron jobs, which one would think are essential to something like Prefect, basically can't be done (as of a month ago) without touching the Prefect UI. This is extremely odd.

I also found it fairly confusing which components were supposed to be checked into source control, and which weren't. I blame this on Python deployment generally being very odd and confusing, but Prefect docs don't make it any more clear. Prefect assumes that there's an S3-like storage that both the submitting computer (my laptop) and the orchestrator (the server) can access.

Overall I find it quite handy, and probably won't switch. It feels more lightweight than say using full Docker containers, which we probably don't need right now. The UI is nicer than Airflow's, and the orchestrator & agent are much easier on resources. It feels more reproducible. I haven't tried Prefect Cloud, and we're unlikely to (security & cost are the main reasons).

Re: Dabbling with Dagster vs. Airflow

#20

Where is a service that doesn't focus specifically on 1.) data pipelines / data-science 2.) cicd / build pipelines 3.) ... you name it I mean, just a service that gives me all the groundwork to build one of the above myself. Is there something like that?

I'd say give Temporal (https://temporal.io) a look, but there are a lot of options (https://github.com/meirwah/awesome-workflow-engines).
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