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Amazon MWAA - Managed Workflows for Apache Airflow

Amazon MWAA is a managed Apache Airflow service for orchestrating data pipelines and workflows.

Application Integration
Pricing Model Pay per environment hour and worker
Availability All major regions
Data Sovereignty EU regions available
Reliability 99.9% availability SLA

What is Amazon MWAA?

Amazon MWAA (Managed Workflows for Apache Airflow) is a managed service that provides Apache Airflow as a fully managed cloud solution. Apache Airflow is the most widely adopted open-source platform for orchestrating data pipelines, ETL workflows, and ML pipelines.

Operating a self-managed Airflow installation is complex: web server, scheduler, workers, and metadata database must be configured, monitored, and scaled. MWAA handles all of this infrastructure and additionally provides automatic scaling of worker capacity based on workload.

Existing Airflow DAGs (Directed Acyclic Graphs) can be adopted in MWAA without modifications. DAG files are stored in an S3 bucket, and the MWAA environment loads them automatically. Custom Python packages and Airflow plugins are also supported.

Core Features

  • Fully Managed: Automatic provisioning and maintenance of web server, scheduler, and worker infrastructure
  • Auto Scaling: Worker capacity scales automatically based on the number of parallel tasks
  • S3-Based DAGs: DAG files are stored in S3 and loaded automatically, enabling easy CI/CD integration
  • Plugin Support: Custom Airflow plugins and Python packages via requirements.txt
  • Built-in Security: VPC integration, IAM-based access control, and CloudWatch logging

Typical Use Cases

Data Pipeline Orchestration: Data teams orchestrate complex ETL pipelines that extract data from various sources, transform it, and load it into data warehouses or data lakes. Airflow DAGs define dependencies and execution order.

ETL Workflow Automation: Regularly executed data processing jobs are defined as Airflow DAGs with scheduling, error handling, retries, and alerting. MWAA ensures the infrastructure for these workflows is always available.

ML Pipeline Management: Data science teams use MWAA to orchestrate ML pipelines: data preparation, feature engineering, model training, evaluation, and deployment as an automated workflow chain.

Benefits

  • No operational overhead for Airflow infrastructure
  • Existing DAGs can be migrated without modifications
  • Automatic scaling of worker capacity
  • Seamless integration with AWS data and ML services

Integration with innFactory

As an AWS Reseller, innFactory supports you with Amazon MWAA: from migrating existing Airflow environments and developing and optimizing DAGs to integration with AWS data services and building CI/CD pipelines for DAG deployment.

Typical Use Cases

Data pipeline orchestration
ETL workflow automation
ML pipeline management

Frequently Asked Questions

What is Amazon MWAA?

Amazon MWAA (Managed Workflows for Apache Airflow) is a managed service that provides Apache Airflow in the cloud. The service handles setup, operation, and scaling of the Airflow infrastructure so you can focus on developing your workflows.

What Airflow versions are supported?

MWAA supports current Apache Airflow versions and regularly updates to new releases. The Airflow environment can be extended with custom Python packages and plugins.

How are DAGs deployed?

DAGs (Directed Acyclic Graphs) are stored as Python files in an S3 bucket and automatically loaded by the MWAA environment. Changes to DAGs are picked up within seconds.

AWS Cloud Expertise

innFactory is an AWS Reseller with certified cloud architects. We provide consulting, implementation, and managed services for AWS.

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