Amazon Managed Workflows for Apache Airflow, known as Amazon MWAA, is a service that enhances security, availability, and reduces the management overhead when setting up data pipelines in the cloud. The announcement of Apache Airflow version 2.9.2 on Amazon MWAA brings new features like logical operators for DAG scheduling, combining data and time-based schedules, and custom names for Dynamic Task Mapping.
The logical operators introduced in Airflow 2.9.2 allow users to define scheduling conditions based on dataset updates, enabling more precise workflow triggers. This logic is beneficial for scenarios like risk management in the financial industry, where tasks need to run based on specific combinations of dataset updates.
Combining dataset and time-based schedules is another feature in Airflow 2.9.2 that allows workflows to run not just at set intervals but also when there are updates to specific datasets. This ensures that data pipelines are triggered based on the latest data and changes.
The addition of dataset event REST API endpoints enables external systems to interact with Amazon MWAA environments, improving data pipeline management. By initiating dataset-related events programmatically, users can enhance connectivity and responsiveness in workflows.
Airflow 2.9.2 also introduces operational features like DAG auto-pausing, CLI support for bulk pausing and resuming of DAGs, and custom names for Dynamic Task Mapping. These features enhance the automation and management of complex data pipelines, reducing operational overhead and human error in DAG management.
The upgrade to Airflow 2.9.2 in Amazon MWAA can be done through various methods like the AWS Management Console, API, AWS CLI, or IaC tools like AWS CloudFormation or Terraform. Existing Airflow environments can be upgraded to version 2.9 using in-place version upgrades, allowing users to leverage the new capabilities seamlessly.
Overall, the latest release of Apache Airflow in Amazon MWAA offers advanced features and enhancements that enable users to build more dynamic and responsive data pipelines. The availability of new scheduling options, REST API endpoints, and operational improvements make it easier to manage and operate end-to-end workflows efficiently.
Article Source
https://aws.amazon.com/blogs/big-data/introducing-amazon-mwaa-support-for-apache-airflow-version-2-9-2/