Airflow allows workflows to be written as Directed Acyclic Graphs (DAGs) using the Python programming language. Airflow is integrated with AWS Security services to provide fast and secure access to your data.Īmazon MWAA uses the Amazon VPC, DAG code, and supporting files in your Amazon S3 storage bucket to create an environment. Amazon MWAA is capable of automatically scaling Airflow’s workflow execution capacity to meet your needs. With Amazon Managed Workflows for Apache Airflow, you can author, schedule, and monitor workflows using Airflow within AWS without having to set up and maintain the underlying infrastructure. What are Managed Workflows for Apache Airflow (MWAA)?Īmazon Managed Workflows for Apache Airflow is a fully managed service in the AWS Cloud for deploying and rapidly scaling open-source Apache Airflow projects. Scalability: Airflow is highly scalable and is designed to support multiple dependent workflows simultaneously.It uses Jinja templates to create pipelines and it further makes it easy to keep track of the ongoing tasks. Rich User Interface: Airflow’s rich User Interface (UI) helps in monitoring and managing complex workflows.You can also extend the libraries as per your needs so that it fits the desired level of abstraction. Customizability: Airflow supports customization, and it allows users to design their own custom Operators, Executors, and Hooks. It can also easily integrate with other platforms like Amazon AWS, Microsoft Azure, Google Cloud, etc. This allows Airflow to be integrated with several operators, hooks, and connectors to generate dynamic pipelines. Dynamic Integration: Airflow uses Python programming language for writing workflows as DAGs. It comes with a large community of active users that makes it easier for developers to access resources.
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