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How LaunchDarkly migrated to Amazon MWAA to achieve efficiency and scale
- LaunchDarkly migrated to Amazon MWAA to scale their internal analytics platform, handling up to 14,000 tasks per day with minimal cost increase.
- Challenges faced included difficulties with time to integrate new AWS services, data locality issues, and non-centralized orchestration across engineering teams.
- The solution involved a central Amazon MWAA environment for orchestration, ECS cluster for running tasks, CloudWatch and Datadog for monitoring, and more.
- Migration involved transitioning Airflow code from 1.12 to 2.5.1, faced issues with custom operator dependencies, and improved isolation by moving tasks to ECS on Fargate.
- Upgrade to Airflow 2 was successful, with minimal cost increase due to ECS usage, delivering improved monitoring and observability with Datadog and CloudWatch.
- Scaling beyond internal analytics, LaunchDarkly used Amazon MWAA as a generic orchestrator for various services, enhancing scalability and onboarding speed.
- Lessons learned included the importance of isolation for reliability, monitoring, and observability, leading to successful scaling to 14,000 tasks per day.
- Future plans involve further integration with AWS services like Lambda and SQS to automate data workflows, supporting greater scalability as the company expands.
- By adopting managed services like Amazon MWAA and best practices, LaunchDarkly accelerated innovation, mitigated risks, and improved time-to-value of product offerings.
- Organizations looking to modernize data pipelines should assess current setups, explore Amazon MWAA capabilities, and leverage containerization for improved workflows.
- Authors include Asena Uyar, Dean Verhey, and Daniel Lopes, discussing their journey of orchestrating data pipelines at scale with Amazon MWAA and Amazon ECS.
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