5/30/2023 0 Comments Cloud composer![]() This includes being notified when a failure occurs and recovering from it. Failure management and monitoring: in a production environment it is really important to consider how to manage failures.Furthermore, Kubeflow Pipelines has a really limited usage of macros and no built-in templating system. ![]() This makes it complicated if not sometimes impossible to create specific dependencies between tasks such as branching a task or executing on condition. Implementing complex data processing workflows in Kubeflow Pipelines is possible but more complicated as the SDK, based on Argo, uses python to create a YAML file behind the scenes. In addition to that Airflow provides strong templating capabilities through Jinja2 and Airflow macros. A DAG in Airflow can be defined directly as Python code. Airflow makes it easy to build a DAG with complex dependencies, something that may be required especially when orchestrating ingestion/preprocessing/feature engineering tasks.
0 Comments
Leave a Reply. |