A Look At The Data Systems Behind The Gameplay For League Of Legends

Summary The majority of blog posts and presentations about data engineering and analytics assume that the consumers of those efforts are internal business users accessing an environment controlled by the business. In this episode Ian Schweer shares his experiences at Riot Games supporting player-focused features such as machine learning models and recommeder systems that are deployed as part of the game binary. He explains the constraints that he and his team are faced with and the various challenges that they have overcome to build useful data products on top of a legacy platform where they don’t control the end-to-end systems. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. 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All of this, plus its transparent pricing and 24*7 live support, makes it consistently voted by users as the Leader in the Data Pipeline category on review platforms like G2. Go to dataengineeringpodcast.com/hevodata and sign up for a free 14-day trial that also comes with 24×7 support. Your host is Tobias Macey and today I’m interviewing Ian Schweer about building the data systems that power League of Legends Interview Introduction How did you get involved in the area of data management? Can you describe what League of Legends is and the role that data plays in the experience? What are the characteristics of the data that you are working with? (e.g. volume/variety/velocity, structured vs. unstructured, real-time vs. batch, etc.) What are the biggest data-related challenges that you face (technically or organizationally)? Multiplayer games are very sensitive to latency. How does that influence your approach to instrumentation/data collection in the end-user experience? Can you describe the current architecture of your data platform? What are the notable evolutions that it has gone through over the life of the game/product? What are the capabilities that you are optimizing for in your platform architecture? Given the longevity of the League of Legends product, what are the practices and design elements that you rely on to help onboard new team members? What are the seams that you intentionally build in to allow for evolution of components and use cases? What are the most interesting, innovative, or unexpected ways that you have seen data and its derivatives used by Riot Games or your players? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the data stack for League of Legends? What are the most interesting or informative mistakes that you have made (personally or as a team)? What do you have planned for the future of the data stack at Riot Games? Contact Info LinkedIn Github Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Riot Games League of Legends Team Fight Tactics Wild Rift DoorDash Podcast Interview Decision Science Kafka Alation Airflow Spark Monte Carlo Podcast Episode libtorch The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By:Hevo:  Are you sick of repetitive, time-consuming ELT work? Step off the hamster wheel and opt for an automated data pipeline like Hevo. Hevo is a reliable and intuitive data pipeline platform that enables near real-time data movement from 150+ disparate sources to the destination of your choice. Hevo lets you set up pipelines in minutes, and its fault-tolerant architecture ensures no fire-fighting on your end. The pipelines are purpose-built to be ‘set and forget,’ ensuring zero coding or maintenance to keep data flowing 24×7. All it takes is 3 steps for your pipeline to be up and running. 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