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How Flutter UKI optimizes data pipelines with AWS Managed Workflows for Apache Airflow
- Flutter UKI transitioned from a monolithic Amazon EC2-based Airflow setup to Amazon Managed Workflows for Apache Airflow (Amazon MWAA) for improved scalability and optimization.
- Flutter UKI, as a part of Flutter Entertainment, operates in the sports betting and gaming industry with a strong online presence as well as physical betting shops.
- Their Data team plays a crucial role in utilizing data for business success by creating robust data pipelines and maintaining high data quality standards.
- The migration to Amazon MWAA involved thorough proof-of-concept testing, collaboration with AWS Enterprise Support, and a phased deployment approach.
- They managed over 3,500 dynamically generated DAGs through intelligent load balancing across multiple Amazon MWAA environments, ensuring scalable infrastructure.
- Key optimizations included using Kubernetes Pod Operator, a wrapper around it for simplification, monthly image updates, continuous Airflow updates, and CI/CD integration.
- Operational excellence was achieved through a comprehensive monitoring framework with Amazon CloudWatch metrics and early warning alarms.
- Flutter UKI's infrastructure comprises four Amazon MWAA clusters managing over 5,500 DAGs, 30,000 tasks, and handling more than 60,000 DAG runs daily.
- The transition to Amazon MWAA has led to a stable, scalable, and resilient production environment, allowing engineering teams to focus more on business-critical tasks and innovation.
- The post encourages considering Amazon MWAA for a fully managed Airflow solution on AWS and provides resources for exploring and integrating the service.
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