Workflow Management - Amazon Managed Workflows for Apache Airflow (MWAA) - AWS
Short summary
Amazon MWAA runs Airflow pipelines on AWS as a managed service with serverless or provisioned deployment options. It handles infrastructure provisioning, scaling, security, and integrates with SageMaker for end-to-end ML and data workflows.
- MWAA now offers two deployment modes: serverless (pay-per-execution, auto-scaling) and provisioned (full config control with AWS-managed infrastructure)
- It integrates directly with the next-gen SageMaker Unified Studio, letting Airflow run alongside Jupyter notebooks for unified data prep, training, and inference pipelines
- Both modes run inside your VPC with end-to-end encryption and IAM controls, with serverless adding workflow-level permissions and provisioned offering environment-wide team separation
Full summary
Amazon’s Managed Workflows for Apache Airflow (MWAA) gives you a hosted Airflow service with two deployment paths: serverless and provisioned. The serverless option spins up workers on demand to run your YAML or Python DAGs, scales automatically to handle hundreds of workflows, and charges you only for execution time. The provisioned environment hands you full control over Airflow settings, plugins and dependencies while AWS still manages the underlying EC2 instances, scaling, patching and security.
MWAA plugs directly into the next generation of Amazon SageMaker. You get an open-source Airflow instance running alongside your Jupyter notebooks in SageMaker Unified Studio. That setup makes it easy to glue together data prep, model training, batch inference, even hyperparameter tuning jobs—all from the same interface. Automated scaling and fault tolerance in SageMaker ensure your DAGs run reliably, so you can focus on pipeline logic instead of server maintenance.
On the security front, both deployment modes sit inside your VPC, use end-to-end encryption, and support IAM-based access controls. Serverless adds workflow-level permissions, while the provisioned model gives you environment-wide separation for different teams or business units. You get enterprise-grade compliance without writing custom infrastructure code.
Typical use cases range from complex ETL across AWS services and external sources to end-to-end ML pipelines—feature extraction, model validation, A/B testing—and routine tasks like report generation or data archival. You can coordinate streaming and batch jobs, sync data between SaaS apps and your data lake, or automate compliance workflows across cloud and on-premises systems.