Users may design workflows as DAGs (Directed Acyclic Graphs) of tasks using orchestration tools like Airflow. This functionality may also be used to recompute any dataset after making changes to the code. It’s also used to train Machine Learning models, provide notifications, track systems, and power numerous API operations. Airflow also has a backfilling feature that enables users to simply reprocess prior data. Its usefulness, however, does not end there. Airflow has become one of the most powerful open source Data Pipeline solutions available in the market.Īirflow was built to be a highly adaptable task scheduler. Your Data Pipelines‘ dependencies, progress, logs, code, trigger tasks, and success status can all be viewed instantly. It’s one of Data Engineers’ most dependable technologies for orchestrating operations or Pipelines. Airflow Alternatives: AWS Step FunctionsĪpache Airflow is a workflow authoring, scheduling, and monitoring open-source tool.Before you jump to the Apache Airflow Alternatives, let’s discuss what is Airflow, its key features, and some of its shortcomings that led you to this page. You can try out any or all and select the best according to your business requirements. To help you with the above challenges, this article lists down the best Airflow Alternatives along with their key features. With that stated, as the data environment evolves, Airflow frequently encounters challenges in the areas of testing, non-scheduled processes, parameterization, data transfer, and storage abstraction. Thousands of firms use Airflow to manage their Data Pipelines, and you’d be challenged to find a prominent corporation that doesn’t employ it in some way. 3) Airflow Alternatives: AWS Step Functions.inotify (monitoring file system changes and trigger events)Īirflow vs. If you prefer a simple command-line tool to schedule tasks, Than about finding a tool that can adapt to your existing custom workflows. MLFlow if you care more about tracking experiments or tracking and deploying models.KubeFlow if you want to use Kubernetes but still define your tasks with Python instead of YAML.Argo if you're already deeply invested in the Kubernetes ecosystemĪnd want to manage all of your tasks as pods, defining them in YAML instead of Python. It has fewer features, but it’s easier to get off the ground.
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