Modern Data Stack for Game Analytics / Dmitry Anoshin (Microsoft Gaming, The Coalition)
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The document outlines the importance of a modern data stack in game analytics, detailing the roles of various analytics types and the evolution of data storage solutions. It emphasizes the necessity for analytics in game design and decision-making, highlighting challenges and best practices in data engineering and privacy. Additionally, it provides insights into specific analytics architectures used in the gaming industry, including those employed by 'The Coalition' and references to notable games.
Modern Data Stack for Game Analytics / Dmitry Anoshin (Microsoft Gaming, The Coalition)
- 1. Modern Data Stack for Game Analytics
- 2. Disclaimer All thoughts are mine. Based on my experience and environment I worked over decade.
- 3. Outline and Take Away points Outline: ● About myself and Microsoft ● Data Analytics Framework History ● Game Analytics Intro ● Modern Data Stack Overview ● Architectures from the Industry ● The Coalition Data Stack Takeaways: ● All modern game studios require analytics ● Privacy is critical ● Modern Data Analytics Open source and commercial products ● Reference Architecture for the Game Platform ● Challenges during designing of analytics solution
- 4. - 11+ years in Analytics - Moscow, Montenegro, Winnipeg, Vancouver, Victoria, Seattle, Boston - 5 years @Amazon, now @Microsoft, The Coalition - Tableau, Snowflake, Microsoft, AWS user groups and meetups
- 7. ● Gears of War: Ultimate Edition (XB1, Win10 | 2015 | Metascore 82/73) ● Gears of War 4 (XB1, Win10 | 2016 | Metascore 84/86) ● Gears Pop! (Mobile | 2019 ) ● Gears of War 5 (XB1, Win10 | 2019 | Metascore 85/82) ● Gears Tactics (XB1, PC | 2020 | Metascore 81) ● Gears 5: Hivebusters (XB1, PC | 2020 | Metascore 82)
- 8. What the heck - Analytics!?
- 10. Gaming Data Consumers ● Leadership ● Producers ● Artists ● Game Play Engineers ● QA Engineers ● Community Managers
- 11. Microsoft Privacy and Online Safety https://privacy.microsoft.com/en-ca/privacystatement https://support.xbox.com/en-CA/help/family-online-safety/online-safety/privacy
- 12. 3 Game Analytics Goals Strategic Analytics - target the global view how the game should evolve based on analysis of user behavior and the business model. Tactical Analytics - inform game design at the short term. Operational Analytics - analysis and evaluation in immediate situation.
- 13. Telemetry as a source of Player data The word Telemetry is derived from the Greek roots tele, "remote", and metron, "measure". Games are state machines - a person creates a continual loop of actions and responses which keep the game state changing. Often loops keeping the user engages over a period of time. Telemetry helps to discovering who is performing what action when and where in the game. It cannot provide why.
- 14. 3 types of metrics Gameplay metrics user behavior in the game Community metrics user engagement in communities and social media Customer metrics user as a customer, acquisition and retention
- 15. Action Third-Person Shooters (TPS) Metrics ● Weapon use ● trajectory ● item/asset use ● character/kit choice ● level/map choice ● loss/win ● heatmaps ● team scores ● map lethality ● map balance ● vehicle use metrics ● special moves ● jumps and many more. Death map, Halo3 https://coolinfographics.com/blog/2009/1/12/halo-3- heatmaps.html
- 16. Role of Data Engineer My role is a DE to make sure that we have a infrastructure in place to collect, transform and consolidate data for customer, community and gameplay metrics. The infrastructure is responsible for Strategic and Tactical Analytics during development and post production.
- 17. Key Milestones in the Analytics Industry ● Relational Databases ● Custom software ● MPP Data warehouse ● Enterprise ETL ● Enterprise BI Tools ● Data Mining Tools ● Big Data: Hadoop, Hive, Spark (on-premise) ● Data Lake ● DataScience, R, Python ● Cloud Computing ● AWS Redshift, Azure SQL DW (Synapse), Google BigQuery, Snowflake, Databricks ● ML frameworks ● ETL -> ELT
- 20. Traditional approach Batch (ETL) Source Layer Data Processing Storage Business Business Intelligence ● Ad-hoc queries ● Pixel Perfect Reports ● Cross tables (Pivot) Game Client Data Warehouse
- 21. Data Storage Layer | Data Warehouse SMP - Symmetric Multi-Processing ● Traditionally one server systems ● Data stored locally ● Processors share single OS, memory, I/O devices ● Scale-up only - physical limitations to scaling to accommodate workload MPP - Massively Parallel Processing ● Multi-node(server) systems ● Data stored externally ● Scale-out - add more Compute nodes, each with dedicated CPU, memory & I/O subsystems ● No single point of contention
- 22. Data Storage Layer | Data Lake
- 24. Analytics architecture evolution - Prior 2010 mostly Data Warehouse (SMP, MPP). - With rise of Hadoop - shift towards data lake. Decouple Compute and Storage but lack of ACID (Atomicity, Consistency, Isolation, Durability). - Lake house = Data Warehouse + Data Lake.
- 25. Lakehouse Options ● Transaction Support (ACID) ● Schema Enforcement ● Upserts/Deletes Key solutions: ● Apache Hudi (Hadoop Update Delete and Incremental) by Uber Engineering ● Apache Iceberg by Netflix ● Delta Lake by Apache Spark
- 26. Streaming Batch (ETL/ELT) Modern Data Stack Source Layer Data Processing Storage Science & Experimentation Business Datascience Machine Learning Business Intelligence Game Client
- 27. Streaming Batch (ETL/ELT) Modern Key Layers and roles Source Layer Data Processing Storage Science & Experimentation Business Datascience Machine Learning Business Intelligence Data Engineer ML Engineer Data Scientist BI Engineer Product Manager - manage data product. Game Client
- 28. Modern Data Stack with Open Source Source Layer Data Processing Storage Science & Experimentation Business Spark Pool MLlib Game Client
- 29. Event Hub Stream, Analytics Batch (ETL/ELT) Modern Data Stack with Microsoft Azure Source Layer Data Processing Storage Science & Experimentation Business Spark pools Spark Pool MLlib Azure ML (not in Synapse) Serverless Pool Azure Synapse Studio Dedicated SQL pool Azure Data Lake v2 Serverless Pool
- 30. ADX | Ingesting Modern Data with Azure Data Explorer (ADX) Source Layer Data Processing Storage Science & Experimentation Business ADX | Data Science Azure Data Explorer | Storage Kusto / ADX
- 31. Solution: Fortnite on AWS https://aws.amazon.com/solutions/case-studies/EPICGames/
- 32. Solution: Sega
- 34. Solution: Future Games Studio https://youtu.be/9uec5ujkuCA
- 35. Solution: Electronic Arts Studio https://youtu.be/ot1Qzdszvsc
- 36. Solution: Mobile Game Analytics from GCP https://cloud.google.com/architecture/mobile-gaming-analysis-telemetry
- 38. How it was Source Layer Data Processing Storage Science & Experimentation Business Azure Cloud On-Premise Game Client/Server The Coalition Data Lake
- 39. Event Grid Streaming Azure Data Factory | Batch (ELT) How it is going Source Layer Data Processing Storage Science & Experimentation Business The Coaltion Data Lake Game Client/Server Spark Structured Streaming* Azure Data Lake Storage V2(Compute) Spark MLlib *Spark Structured Streaming – not in production. We are testing it. **Spark Mllib and Mlflow – part of the future vision
- 40. Data Engineering Design Flow as a Funnel Event Names: ● Weapon Use ● Damage ● Shooting ● Flock ● Map Name ● HeartBeat ● and so on Raw Tables (Bronze) Method: Append Trans: Minimum Staging Tables (Silver) Method: Append Trans: JSON Schema Fact Tables (Gold) Method: Merge Trans: Heavy
- 41. ● Cross team collaboration between SDE vs DE, BI vs DE, DE vs DS ● Low data volume before Launch ● Schema Evolution ● Cost and Budgeting ● Security best practice (for example credentials) ● Privacy and compliance (GDPR, HIPAA lack of data form ML/AI) ● Data Quality at Scale (Deequ, Great Expectations) ● Responsible AI Key Challenges
- 42. ● There is no bad solution/vendor ● Focus on business outcome (working backwards) ● Engineering Excellence (dev/prod, CI/CD) ● You can build solution using Code (Python, Java, Scala, SQL and so on) or GUI (with some restrictions). ● Security and Privacy best practices Summary
- 43. For more information visit: https://www.thecoalitionstudio.com/join-us/ The Coalition is looking for talented and diverse people to join our squad, with exciting opportunities across our Art, Design, Engineering, and Production teams.