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Amazon Managed Workflows for Apache Airflow (MWAA)
Simplify workflow orchestration with managed Apache Airflow securely and at scale
Why Amazon MWAA?
Amazon Managed Workflows for Apache Airflow (MWAA) enables you to orchestrate data pipelines and workflows using the industry-standard Apache Airflow platform. Choose between serverless or provisioned deployment options to enjoy enterprise-grade scalability, availability, and security, without managing infrastructure.
Amazon MWAA is accessible in the next generation of Amazon SageMaker
With Amazon MWAA in the next generation of Amazon SageMaker, you can deploy and scale Apache Airflow seamlessly without operational burdens. With automated scaling and built-in fault tolerance, MWAA in Amazon SageMaker ensures your workflows execute reliably—allowing you to focus on innovation, not infrastructure. Learn more.
Choose Your Deployment Model
Serverless Workflows
Amazon MWAA Serverless eliminates infrastructure management complexities by automatically executing your YAML or Python-based workflows on-demand or on schedule. Scale effortlessly to hundreds of workflows with enterprise-grade high availability—no Apache Airflow configuration required. With usage-based pricing and built-in security controls, you only pay for actual execution time.
Traditional Managed Environment
Amazon MWAA empowers you to maintain full control of your workflows by deploying customized environments with preferred dependencies and plugins—while we handle infrastructure provisioning, security, and scaling.
Benefits
Amazon MWAA offers deployment models to meet diverse workflow needs. MWAA Serverless automatically adjusts to demand, eliminating manual capacity planning while optimizing costs. The provisioned option provides granular control over Airflow resources, allowing you to fine-tune performance for specific requirements. Choose effortless automatic scaling or precise resource control—Amazon MWAA scales with your business.
Amazon MWAA delivers comprehensive security tailored to your needs—Serverless provides granular workflow-level controls while Provisioned offers environment-level boundaries for distinct business contexts. Both options feature enterprise-grade security including VPC isolation, end-to-end encryption, and fine-grained access controls, ensuring your workflows remain protected without compromising performance.
Amazon MWAA powers workflows for the next generation of Amazon SageMaker with access to a personal, open-source Airflow deployment, running alongside Jupyter notebooks in Amazon SageMaker Unified Studio. You can easily develop Airflow Directed Acyclic Graphs (DAGs) that can orchestrate their project artifacts such as notebooks, queries, and training jobs.
Use cases
Orchestrate complex data transformations across multiple AWS services and external data sources. Automate data ingestion, cataloging, and processing workflows for your data lake architecture while coordinating streaming data pipelines with batch processing.
Orchestrate end-to-end ML workflows from data preparation through model training, validation, and deployment. Automate feature extraction, transformation workflows, and coordinate model deployment with A/B testing across development and production environments.
Schedule and orchestrate report generation, data refreshes, and dashboard updates automatically. Implement automated data validation, cleansing, and quality assurance workflows while synchronizing data between business applications and analytics platforms.
Automate backup workflows, data archival, and disaster recovery procedures across your infrastructure. Orchestrate data governance workflows, compliance reporting, and audit trail generation while coordinating workflows across AWS and other cloud providers or on-premises systems.