Category: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

Apache Airflow 3.x on Amazon MWAA introduces architectural improvements such as API-based task execution that provides enhanced security and isolation. This migration presents an opportunity to embrace next-generation workflow orchestration capabilities while providing business continuity. This post provides best practices and a streamlined approach to successfully navigate this critical migration, providing minimal disruption to your mission-critical data pipelines while maximizing the enhanced capabilities of Airflow 3.

AWS announced the general availability of Apache Airflow 3 on Amazon Managed Workflows for Apache Airflow (Amazon MWAA). This release transforms how organizations use Apache Airflow to orchestrate data pipelines and business processes in the cloud, bringing enhanced security, improved performance, and modern workflow orchestration capabilities to Amazon MWAA customers. This post explores the features of Airflow 3 on Amazon MWAA and outlines enhancements that improve your workflow orchestration capabilities

Orchestrating machine learning pipelines is complex, especially when data processing, training, and deployment span multiple services and tools. In this post, we walk through a hands-on, end-to-end example of developing, testing, and running a machine learning (ML) pipeline using workflow capabilities in Amazon SageMaker, accessed through the Amazon SageMaker Unified Studio experience. These workflows are powered by Amazon Managed Workflows for Apache Airflow.

In this post, we explore a streamlined, configuration-driven approach to orchestrate dbt Core jobs using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and Cosmos, an open source package. These jobs run transformations on Amazon Redshift. With this setup, teams can collaborate effectively while maintaining data quality, operational efficiency, and observability.

In this post, we explore best practices for upgrading your Amazon MWAA environment and provide a step-by-step guide to seamlessly transition to the latest version.

In this post, we explore how LaunchDarkly scaled the internal analytics platform up to 14,000 tasks per day, with minimal increase in costs, after migrating from another vendor-managed Apache Airflow solution to AWS, using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and Amazon Elastic Container Service (Amazon ECS).

This post shows how to enhance the multi-cluster solution by integrating Amazon Managed Workflows for Apache Airflow (Amazon MWAA) with BPG. By using Amazon MWAA, we add job scheduling and orchestration capabilities, enabling you to build a comprehensive end-to-end Spark-based data processing pipeline.

In this post, we share how Flutter UKI transitioned from a monolithic Amazon Elastic Compute Cloud (Amazon EC2)-based Airflow setup to a scalable and optimized Amazon Managed Workflows for Apache Airflow (Amazon MWAA) architecture using features like Kubernetes Pod Operator, continuous integration and delivery (CI/CD) integration, and performance optimization techniques.

In this post, we explore how to apply the principle of least privilege to your Amazon MWAA environment by tightening network security using security groups, network access control lists (ACLs), and virtual private cloud (VPC) endpoints. We also discuss the Amazon MWAA execution and deployment roles and their respective permissions.

In this blog post, we demonstrate how to use Amazon MWAA for centralized orchestration, while distributing data processing and machine learning tasks across different AWS accounts and Regions for optimal performance and compliance.