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In this lesson we cover Amazon Managed Workflows for Apache Airflow (MWAA): a fully managed orchestration service for Apache Airflow on AWS. What is Amazon Managed Workflows for Apache Airflow (MWAA)?
MWAA removes the operational burden of running the Airflow control plane by provisioning, operating, and scaling the underlying Airflow infrastructure for you. You still author DAGs (directed acyclic graphs) in Python and upload them to an S3 bucket, but AWS manages the scheduler, workers, scaling, high availability, and fault tolerance. You pick the Airflow version and configure environment settings, and MWAA runs the compute required as your workflows grow.
If an exam question asks about a managed Apache Airflow service in AWS, the correct answer is MWAA (Amazon Managed Workflows for Apache Airflow).
MWAA exposes a managed Airflow control plane integrated with the AWS Management Console, AWS CLI, and SDKs so you can configure environments, view logs, and monitor DAG runs without operating the underlying infrastructure.
A presentation slide titled "Why Do We Need Managed Workflow For Apache Airflow (MWAA)?"It lists four benefits—simplified deployment and management, scalability and performance optimization, high availability and fault tolerance, and a managed environment with a control plane—each shown with a matching icon.
Architecture and components When you create an MWAA environment, AWS provisions an Airflow service that behaves like the open-source Airflow distribution but with the operational overhead removed. Key points:
  • DAGs are authored in Python and stored in an S3 bucket that MWAA reads.
  • MWAA runs the scheduler and the worker/executor to coordinate task execution.
  • Tasks in DAGs can extract, transform, and load data, then hand results off to analytics, storage, or ML services.
  • Because DAGs are code, you can programmatically define all steps of your pipelines and version them with your repository and S3.
Typical MWAA deployment flow:
  • Author DAGs locally or in a repository.
  • Upload DAGs and plugins to the configured S3 bucket (MWAA reads from S3).
  • Configure MWAA environment (Airflow version, worker size, network settings).
  • Monitor DAG runs and logs via the console or CloudWatch.
A diagram titled "MWAA Components" showing Amazon S3 and DAGs feeding Amazon MWAA for orchestration, scheduling, and monitoring. The workflow then moves through extract & transform, load data, and learn (ML/analytics) stages with outputs to analytics, storage, databases, and plugins.
Key features and operational details
  • Managed infrastructure: AWS provisions and manages the Airflow control plane and underlying compute.
  • Auto-scaling and availability: MWAA can scale to meet workload demands and is designed for high availability and fault tolerance.
  • Security: Environments run inside your VPC; data at rest can be encrypted with AWS Key Management Service (KMS).
  • Observability: Airflow metrics and logs can be sent to Amazon CloudWatch for centralized monitoring and alerting.
  • Management options: Use the AWS Management Console, AWS CLI, or SDKs to manage environments, variables, and connections.
Example: S3 layout commonly used with MWAA
  • s3://<your-mwaa-bucket>/dags/ — place DAG Python files here
  • s3://<your-mwaa-bucket>/plugins/ — custom operators/hooks/plugins
  • s3://<your-mwaa-bucket>/requirements/requirements.txt — Python dependencies to install on workers
Example minimal Airflow DAG (save to dags/hello_mwaa.py):
Integration with AWS services MWAA integrates natively with many AWS services, enabling DAGs to call or orchestrate native AWS functionality. Common integrations include S3, Glue, Redshift, Lambda, EMR, Athena, Batch, DynamoDB, Kinesis, and more.
A presentation slide titled "MWAA Integration" showing four AWS service icons and labels: Amazon Simple Storage Service (S3), AWS Glue, Amazon Redshift, and "Many Other AWS Services." The icons are arranged in a row across the slide.
Best practices and common use cases Use cases
  • Schedule recurring or ad-hoc workflows that prepare and process data from diverse sources.
  • Orchestrate multi-step ETL pipelines that span several AWS and third-party services.
  • Automate data preparation pipelines for machine learning: preprocessing, feature engineering, training, and model retraining.
Best practices
  • Keep DAGs lightweight; offload heavy-processing tasks to EMR / Batch / Lambda where appropriate.
  • Store sensitive settings in Airflow connections or use AWS Secrets Manager and IAM roles.
  • Pin an Airflow version for compatibility with custom plugins/operators and test upgrades in a sandbox environment.
  • Use CloudWatch metrics and logs for alerting on task failures and scheduler health.
A presentation slide titled "MWAA – Use Cases" showing three dashed boxes labeled "Support Complex Workflows," "Coordinate Extract, Transform, and Load (ETL) Jobs," and "Prepare ML Data," each with a corresponding icon. The slide is branded "© Copyright KodeKloud."
Be careful with Airflow version upgrades: custom operators, plugins, and third‑party packages may not be compatible across Airflow releases. Test upgrades in a non-production environment before applying to production.
Summary MWAA provides a managed Airflow environment that reduces operational overhead, improves security and observability via AWS integrations, and scales to meet your orchestration needs. It is the recommended AWS-managed solution for running Airflow-based orchestration and data pipelines. Links and references

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