A Look At The Data Systems Behind The Gameplay For League Of Legends - Episode 344


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.

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  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
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  • Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., are already available as plug-and-play connectors with reliable, intuitive SaaS solutions. Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance. Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. 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


  • 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?

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Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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