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

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.

Amazon Web Services (AWS) has been recognized as a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools. We were positioned in the Challengers Quadrant in 2023. This recognition, we feel, reflects our ongoing commitment to innovation and excellence in data integration, demonstrating our continued progress in providing comprehensive data management solutions.

This blog post discusses how a programmatic data processing framework developed by Wipro can help data engineers overcome obstacles and streamline their organization’s ETL processes. The framework leverages Amazon EMR improved runtime for Apache Spark and integrates with AWS Managed services.

ANZ Institutional Division has transformed its data management approach by implementing a federated data platform based on data mesh principles. This shift aims to unlock untapped data potential, improve operational efficiency, and increase agility. The new strategy empowers domain teams to create and manage their own data products, treating data as a valuable asset rather than a byproduct. This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division.

Today, we’re excited to announce mw1.micro, the latest addition to Amazon MWAA environment classes. This offering is designed to provide an even more cost-effective solution for running Airflow environments in the cloud. With mw1.micro, we’re bringing the power of Amazon MWAA to teams who require a lightweight environment without compromising on essential features. In this post, we’ll explore mw1.micro characteristics, key benefits, ideal use cases, and how you can set up an Amazon MWAA environment based on this new environment class.