![]() ![]() That’s why we decided to use Jenkins to create a pipeline workflow. An automated pipeline removes manual errors, provides standardised development feedback loops and enables fast product iterations. Once we release the pipeline, teams can work independently. The ability to release on demand is a critical competency. However, a deployment pipeline can ensure that we have a rollback criteria, versioning, artifacts storage, transaction in the deployment and more. Cron job can detect git changes and update the DAGs folders. Choosing one instead of the other is based on the project complexity. These two approaches are essentially different. Building a deployment pipeline with Jenkins to copy the DAGs.Kubernetes cron job to pull changes from Git into the mounted DAGs directory.There are several different ways to continuously deploy DAGs when running Airflow on Kubernetes: We store DAG code in Git and we want Airflow to be updated with the latest code changes called continuous delivery style. How to make it easy to deploy Airflow DAGs How to make it easy to deploy Airflow DAGs.There are three important lessons we have learned along the way that I would like to share with you today: We realised that we should eliminate redundant systems and build a generic pipeline creation mechanism, for solving these hard problems. I found myself working on a couple of different pipeline implementations and trying to solve the same hard problems over and over again. However, with its great power, Airflow also presents great complexity and many pitfalls. At Natural Intelligence we use Airflow extensively in order to create simple ETL flows as well as complex ML tasks that run on terabytes of data. ![]()
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