So I went on and googled a little bit to find another solution suggested by Airflow which is named Packaged-DAGs. And not only that, I used the Dataclasses package which requires Python 3.7 but on the production instance, we only had Python 3.6 installed. Due to this package dependency issue, the workflows cannot run on the same Python instance. We tried that but my tasks required a different Pandas version than a colleague’s task. One way is adding a requirements.txt file for each workflow, which gets installed on all Airflow workers on deployment. “Hm how am I gonna deploy my workflow to our production instance? How are my packages and other dependencies installed there?” This started very well, but after a while, I thought Obviously, I heavily used the PythonOperator for my tasks as I am a Data Scientist and Python lover. Airflow offers a set of operators out of the box, like a BashOperator and PythonOperator just to mention a few. In Airflow, you implement a task using Operators. When you create a workflow, you need to implement and combine various tasks. I start this article with a short story about myself and Airflow. I hope this leads to reducing your Aspirin consumption in the future as it did for me:) Airflow the Bad Way For sure, I will also show you how you can easily fix that. In this article, I gonna tell you why this is an issue. Why? In short, we use it for both orchestrating workflows and running tasks on the same Airflow instance. This misusage leads to headaches, especially when it comes to workflow deployments. But, I initially used it the wrong way, and probably others also do. This allows you to version your workflows in a source control system like Git, which is super handy.Īll in all, Airflow is an awesome tool and I love it. You configure a workflow in code using Python. As an example, think of an extract, transform, load (ETL) job as a workflow/DAG with the E, T, and L steps being its tasks. A workflow is a sequence of tasks represented as a Direct Acyclic Graph (DAG). It is a platform to programmatically author, schedule, and monitor workflows. The de-facto standard tool to orchestrate all that is Apache Airflow. Data pipelines and/or batch jobs that process and move data on a scheduled basis are well known to all us data folks.
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