This submit is co-written with Monica Cujerean and Ionut Hedesiu from Flutter UKI.
On this submit, 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 strategies.
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 gives 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 by means of an array of famend on-line manufacturers. These embody the enduring 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 crew 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 knowledge 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 person 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 advanced monolithic cluster that demanded fixed consideration and useful resource scaling. The operational overhead of managing these EC2 situations grew to become 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 rework 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 aims fairly than infrastructure administration. By way of in depth proof-of-concept (POC) testing and shut collaboration with AWS Enterprise Assist, Flutter UKI gained confidence within the means of Amazon MWAA to deal with their refined workloads at scale. Their alternative of MWAA over a self-managed answer on Amazon EKS mirrored Flutter UKI’s strategic deal with utilizing managed providers to cut back operational complexity and speed up innovation.
The migration to Amazon MWAA adopted a methodical method. There was in depth testing of a number of POCs. In the course of the POCs, the engineering crew discovered MWAA to have ease of use, which helped them cut back the training 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 crew expanded their deployment over time, regularly transferring a whole lot and finally 1000’s of workflows to the managed service. This cautious, phased transition allowed them to validate the efficiency and reliability of MWAA whereas minimizing operational threat.
Excessive-level structure design
In the course of the service re-architecture, the info crew 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 setting. Another excuse for having a number of environments was to ensure that nobody MWAA setting doesn’t get overloaded by a number of DAGs. By inserting DAG recordsdata throughout numerous Amazon Easy Storage Service (Amazon S3) places and configuring distinctive DAG_FOLDER paths for every setting, the info crew created an clever load balancing mechanism that allocates workflows primarily based on advanced standards together with setting sort, process quantity, and environment-specific DAG affinity. A round-robin distribution technique was designed to attenuate single setting load, making certain scalable infrastructure with zero efficiency degradation. This method allowed the crew 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 setting 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 resolution simplified their structure by eliminating pointless complexity, decreasing upkeep overhead, and mitigating potential bugs. Moreover, this method enabled them to allocate compute assets on a per-task foundation, optimizing general service efficiency. It additionally enabled using completely different container photographs for various duties, thereby avoiding library dependency conflicts.
- Kubernetes Pod Operator wrapper (KPOw): As a substitute of utilizing KPO immediately, 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 fairly than 1000’s of particular person DAGs. The wrapper additionally simplifies DAGs by hiding repetitive parameters, similar to node affinity, pod assets, and EKS cluster configurations. Moreover, it enforces company-specific naming conventions and permits for parameter validation at process execution time fairly than throughout DagBag refresh. Additionally they launched profiles and picture recordsdata, the place profile recordsdata include needed KPO parameters, and the corresponding picture recordsdata 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 in depth code modifications resulting from deprecated libraries.
- Steady Airflow updates: Flutter UKI maintains a cutting-edge infrastructure by implementing new Airflow variations shortly after launch, whereas following a fastidiously orchestrated deployment technique. Their method makes use of commonplace Amazon MWAA configurations and employs a scientific testing protocol. New variations are first deployed to improvement and take a look at environments for thorough validation earlier than reaching manufacturing techniques. This methodical development considerably reduces the chance of disruptions to business-critical workflows.
To attain operational excellence, Flutter UKI has applied a complete monitoring framework centered on Amazon CloudWatch metrics. Their monitoring answer contains strategically configured alarms that present early warning indicators for potential points. This proactive monitoring method allows their groups to shortly establish and examine anomalies in manufacturing workload executions, making certain excessive availability and efficiency of their knowledge pipelines. The mixture 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 improvement 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 recordsdata are saved within the S3 DAG. Putting code outdoors of duties was averted, together with parameter analysis. Parameters and secrets and techniques are saved in AWS Secrets and techniques Supervisor and retrieved at process runtime. Engineers goal to attenuate or get rid of inter-service dependencies inside MWAA.
DAGs are scheduled to distribute execution occasions 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 process pods are used.
Outcomes
At this time, Flutter UKI’s infrastructure contains 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 day by day with a concurrency exceeding 450 duties operating 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, attaining 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 setting. The cautious re-architecting of Flutter UKI’s service, mixed with strategic selections round process execution and infrastructure administration, has not solely simplified their operations, but in addition enhanced efficiency and reliability. Safety and compliance advantages have been additionally seen, as a result of MWAA offers managed safety updates, built-in encryption, and integration with AWS safety providers. 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 improvement and enhancing knowledge pipeline effectivity, finally accelerating innovation of their core enterprise operations.
In the event you’re seeking to cut back operational overhead and migrate to a totally managed Airflow answer on AWS, think about using Amazon MWAA. Get in contact along with your Technical Account Supervisor or your Options Architect to debate an answer particular to your use-case. You can even attain out to AWS Assist by making a case for those who’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 reveals you easy 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, price effectiveness, and new function 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, answerable for designing strategic options to cowl advanced and diverse enterprise wants. His primary experience is on Amazon MWAA, Kubernetes, Amazon Sagemaker, and ETL options.
Nidhi Agrawal is a Technical Account Supervisor at AWS and works with massive 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 personal and public sectors. John helps drive end-to-end buyer engagement by means of program administration excellence. By understanding and representing prospects’ strategic visions, John aligns to develop the folks, organizational readiness, and expertise competencies to satisfy the specified outcomes.
Sidhanth Muralidhar is a Principal Technical Account Supervisor at AWS. He works with massive enterprise prospects who run their workloads on AWS. He’s keen about working with prospects and serving to them architect workloads for price, reliability, efficiency, and operational excellence at scale of their cloud journey. He has a eager curiosity in knowledge analytics as effectively.