This publish is co-written with Monica Cujerean and Ionut Hedesiu from Flutter UKI.
On this publish, 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) structure utilizing options like Kubernetes Pod Operator, steady integration and supply (CI/CD) integration, and efficiency optimization methods.
About Flutter UKI
As a division of Flutter Leisure, Flutter UKI stands on the forefront of the sports activities betting and gaming business. Flutter UKI affords a various portfolio of leisure choices, encompassing sports activities wagering, on line casino video games, bingo, and poker experiences. Flutter UKI’s digital presence is powerful, working via an array of famend on-line manufacturers. These embody the long-lasting Paddy Energy, Sky Betting and Gaming, and Tombola. Whereas Flutter UKI has established a powerful on-line foothold, it maintains a big bodily presence with a community of 576 Paddy Energy betting retailers strategically positioned throughout the UK and Eire.
The Information staff at Flutter UKI is integral to the corporate’s mission of utilizing knowledge to drive enterprise success and innovation. Specializing in knowledge, their groups are devoted to making sure the seamless integration, administration, and accessibility of information throughout a number of aspects of the group. By growing sturdy knowledge pipelines and sustaining excessive knowledge high quality requirements, Flutter UKI empowers stakeholders with dependable insights, optimizes operational efficiencies, and enhances the consumer expertise. Its dedication to knowledge excellence underpins its efforts to stay on the forefront of the net gaming and leisure business, delivering worth and strategic benefit to the enterprise.
The journey from self managing Airflow on Amazon EC2 to working Airflow workloads at scale utilizing Amazon MWAA
Flutter UKI’s knowledge orchestration story started in 2017 with a modest Apache Airflow deployment on EC2 situations. As the corporate’s digital footprint expanded, so did their knowledge pipeline necessities, resulting in an more and more complicated monolithic cluster that demanded fixed consideration and useful resource scaling. The operational overhead of managing these EC2 situations turned a big problem for his or her engineering groups. In 2022, Flutter UKI reached a crossroads. They wanted to decide on between re-architecting their service on Amazon Elastic Kubernetes Service (Amazon EKS) or embracing Amazon Managed Workflows for Apache Airflow (MWAA).
Flutter UKI was seeking to remodel their knowledge orchestration service from a resource-intensive, self-managed system to a extra environment friendly, managed service that may enable them to deal with their core enterprise targets quite than infrastructure administration. By way of intensive proof-of-concept (POC) testing and shut collaboration with AWS Enterprise Help, Flutter UKI gained confidence within the capability of Amazon MWAA to deal with their subtle workloads at scale. Their selection of MWAA over a self-managed resolution on Amazon EKS mirrored Flutter UKI’s strategic deal with utilizing managed companies to cut back operational complexity and speed up innovation.
The migration to Amazon MWAA adopted a methodical method. There was intensive testing of a number of POCs. Throughout the POCs, the engineering staff discovered MWAA to have a great ease of use, which helped them scale back the educational curve leading to quicker. Studying from every POC, they iterated on the ultimate structure by making data-driven selections. Beginning with a small subset of directed acyclic graphs (DAG), the Flutter UKI staff expanded their deployment over time, step by step transferring lots of and finally hundreds of workflows to the managed service. This cautious, phased transition allowed them to validate the efficiency and reliability of MWAA whereas minimizing operational danger.
Excessive-level structure design
Throughout the service re-architecture, the info staff strategically managed over 3,500 dynamically generated DAGs by implementing a classy distribution method throughout a number of Amazon MWAA environments to create a workload remoted atmosphere. One more reason for having a number of environments was to be sure that nobody MWAA atmosphere doesn’t get overloaded by a number of DAGs. By putting DAG information throughout various Amazon Easy Storage Service (Amazon S3) places and configuring distinctive DAG_FOLDER paths for every atmosphere, the info staff created an clever load balancing mechanism that allocates workflows primarily based on complicated standards together with atmosphere sort, job quantity, and environment-specific DAG affinity. A round-robin distribution technique was designed to attenuate single atmosphere load, making certain scalable infrastructure with zero efficiency degradation. This method allowed the staff to optimize workflow orchestration, sustaining excessive efficiency whereas effectively managing an in depth assortment of dynamically generated DAGs throughout a number of MWAA environments. To supply extra compute to particular person duties and to maintain the MWAA environment friendly, Flutter UKI delegated the DAG execution to an exterior compute atmosphere utilizing Amazon Elastic Kubernetes Service (Amazon EKS). The ensuing high-level structure is proven within the following determine.
- Kubernetes Pod Operator (KPO) for duties: Flutter UKI transitioned from utilizing customized operators and plenty of native Airflow operators to completely using the Kubernetes Pod Operator (KPO). This choice simplified their structure by eliminating pointless complexity, lowering upkeep overhead, and mitigating potential bugs. Moreover, this method enabled them to allocate compute sources on a per-task foundation, optimizing general service efficiency. It additionally enabled the usage of completely different container pictures for various duties, thereby avoiding library dependency conflicts.
- Kubernetes Pod Operator wrapper (KPOw): As an alternative of utilizing KPO instantly, they developed a wrapper (KPOw) round it. This wrapper abstracts the underlying complexity and minimizes the impression of signature modifications in Airflow, Amazon MWAA, Amazon EKS, or operator variations. By centralizing these modifications, they solely must replace the wrapper quite than hundreds of particular person DAGs. The wrapper additionally simplifies DAGs by hiding repetitive parameters, equivalent to node affinity, pod sources, and EKS cluster configurations. Moreover, it enforces company-specific naming conventions and permits for parameter validation at job execution time quite than throughout DagBag refresh. Additionally they launched profiles and picture information, the place profile information include needed KPO parameters, and the corresponding picture information hyperlink to the repository for the duty’s container picture. This setup ensures consistency throughout duties utilizing the identical profile and facilitates simultaneous updates throughout duties.
- Month-to-month picture updates in Kubernetes: Imposing a coverage of month-to-month picture updates made certain that their code remained present, stopping safety vulnerabilities and avoiding intensive code modifications attributable to deprecated libraries.
- Steady Airflow updates: Flutter UKI maintains a cutting-edge infrastructure by implementing new Airflow variations shortly after launch, whereas following a rigorously orchestrated deployment technique. Their method makes use of commonplace Amazon MWAA configurations and employs a scientific testing protocol. New variations are first deployed to growth and check environments for thorough validation earlier than reaching manufacturing methods. This methodical development considerably reduces the danger of disruptions to business-critical workflows.
To realize operational excellence, Flutter UKI has applied a complete monitoring framework centered on Amazon CloudWatch metrics. Their monitoring resolution consists of strategically configured alarms that present early warning indicators for potential points. This proactive monitoring method allows their groups to rapidly establish and examine anomalies in manufacturing workload executions, making certain excessive availability and efficiency of their knowledge pipelines. The mix of cautious model administration and sturdy monitoring exemplifies Flutter UKI’s dedication to operational excellence of their cloud infrastructure.
- CI/CD integration: By managing their code in GitLab, with necessary code evaluations and utilizing Argo Occasions and Argo Workflows for picture updates in AWS ECR, they streamlined their growth processes.
- Efficiency Optimization: A good portion of the DAGs are dynamically generated primarily based on database metadata. This technology course of runs outdoors Amazon MWAA, with its personal CI/CD pipeline, and the ensuing DAG information are saved within the S3 DAG. Inserting code outdoors of duties was prevented, together with parameter analysis. Parameters and secrets and techniques are saved in AWS Secrets and techniques Supervisor and retrieved at job runtime. Engineers purpose to attenuate or get rid of inter-service dependencies inside MWAA.
DAGs are scheduled to distribute execution instances as evenly as doable. Job code and customary modules are hosted on Amazon S3 and retrieved at runtime. For bigger codebases, Amazon Elastic File System (Amazon EFS) volumes are mounted to job pods are used.
Outcomes
As we speak, Flutter UKI’s infrastructure includes 4 Amazon MWAA clusters, every executing duties on devoted Amazon EKS node teams. They handle roughly 5,500 DAGs encompassing over 30,000 duties, dealing with greater than 60,000 DAG runs each day with a concurrency exceeding 450 duties working concurrently throughout clusters. They anticipate a ten% month-to-month enhance on this workload within the quick to medium time period. Throughout main occasions like Cheltenham and Grand Nationwide, the place knowledge load will increase by 30%, their MWAA service has demonstrated stability and scalability, reaching a 100% success fee for crucial processes in 2025, a big enchancment over earlier years.
Conclusion
Flutter UKI’s journey with AWS Managed Workflows for Apache Airflow (Amazon MWAA) has resulted in a secure, scalable, and resilient manufacturing atmosphere. The cautious re-architecting of Flutter UKI’s service, mixed with strategic selections round job execution and infrastructure administration, has not solely simplified their operations, but additionally enhanced efficiency and reliability. Safety and compliance advantages had been additionally seen, as a result of MWAA supplies managed safety updates, built-in encryption, and integration with AWS safety companies. Maybe most significantly, the shift to MWAA has allowed Flutter UKI’s engineering groups to redirect their efforts from infrastructure upkeep to business-critical duties, specializing in DAG growth and bettering knowledge pipeline effectivity, in the end accelerating innovation of their core enterprise operations.
Should you’re seeking to scale back operational overhead and migrate to a totally managed Airflow resolution on AWS, think about using Amazon MWAA. Get in contact together with your Technical Account Supervisor or your Options Architect to debate an answer particular to your use-case. It’s also possible to attain out to AWS Help by making a case when you’re dealing with an points organising the service.
Able to see what Amazon MWAA is like? Go to the AWS Administration Console for Amazon MWAA. For extra info, see What Is Amazon Managed Workflows for Apache Airflow. Moreover, Utilizing Amazon MWAA with Amazon EKS exhibits you methods to combine Amazon MWAA with Amazon EKS.
Concerning the authors
Monica Cujerean is a Principal Information Engineer at Flutter UKI, specializing in service associated initiatives that cowl efficiency optimization, value effectiveness, and new characteristic adoption on most AWS service in our stack: Amazon MWAA, Amazon Redshift, Amazon Aurora, and Amazon SageMaker.
Ionut Hedesiu is a Senior Information Architect at Flutter UKI, liable for designing strategic options to cowl complicated and diverse enterprise wants. His principal experience is on Amazon MWAA, Kubernetes, Amazon Sagemaker, and ETL options.
Nidhi Agrawal is a Technical Account Supervisor at AWS and works with giant enterprise prospects to supply the technical steering, greatest practices, and strategic assist to prospects, serving to them optimize their environments within the AWS Cloud.
John Kellett is a Senior Buyer Options Supervisor with 25 years of expertise throughout non-public and public sectors. John helps drive end-to-end buyer engagement via program administration excellence. By understanding and representing prospects’ strategic visions, John aligns to develop the individuals, organizational readiness, and expertise competencies to satisfy the specified outcomes.
Sidhanth Muralidhar is a Principal Technical Account Supervisor at AWS. He works with giant enterprise prospects who run their workloads on AWS. He’s captivated with working with prospects and serving to them architect workloads for value, reliability, efficiency, and operational excellence at scale of their cloud journey. He has a eager curiosity in knowledge analytics as properly.