Deploy Apache Airflow at scale without the operational burden of managing underlying infrastructure.
Run Apache Airflow workloads in your own isolated and secure cloud environment.
Monitor environments through Amazon CloudWatch integration to reduce operating costs and engineering overhead.
Connect to AWS, cloud, or on-premises resources through Apache Airflow providers or custom plugins.
How it works
Amazon Managed Workflows for Apache Airflow (MWAA) orchestrates your workflows using Directed Acyclic Graphs (DAGs) written in Python. You provide MWAA an Amazon Simple Storage Service (S3) bucket where your DAGs, plugins, and Python requirements reside. Then run and monitor your DAGs from the AWS Management Console, a command line interface (CLI), a software development kit (SDK), or the Apache Airflow user interface (UI).
Support complex workflows
Create scheduled or on-demand workflows that prepare and process complicated data from big data providers.
Coordinate extract, transform, and load (ETL) jobs
Orchestrate multiple ETL processes that use diverse technologies within a complex ETL workflow.
Prepare ML data
Automate your pipeline to help machine learning (ML) modeling systems ingest and then train on data.
How to get started
Explore the features
Learn about straightforward Apache Airflow deployment, automatic scaling, security, and more.
Start building with a free account
Get instant access to the AWS Free Tier.
Start using MWAA
Get started building with Amazon MWAA in the console.