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Databricks > Case Studies > Revolutionizing Gaming Experience with IoT: A Case Study on Devsisters' Cookie Run
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Revolutionizing Gaming Experience with IoT: A Case Study on Devsisters' Cookie Run

Technology Category
  • Analytics & Modeling - Machine Learning
  • Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
  • Cement
  • Construction & Infrastructure
Applicable Functions
  • Maintenance
  • Warehouse & Inventory Management
Use Cases
  • Picking, Sorting & Positioning
  • Time Sensitive Networking
Services
  • Data Science Services
The Challenge
Devsisters, a global entertainment and gaming app developer, launched Cookie Run in 2013, which quickly became a success with 2.9 million daily active users in Korea and over 10 million downloads within 12 weeks of its launch. However, as the user base grew, Devsisters' on-premises platform began to experience performance issues and became complex to manage and costly to maintain. The company needed a new data analytics platform that data platform engineers and data scientists could use to analyze big data more quickly and seamlessly. The existing infrastructure was associated with high maintenance costs and frequent issues that required support from software engineers, leading to a diversion of resources from enhancements to troubleshooting. Devsisters attempted to solve this by building its own SQL-based data querying environment based on Spark Thrift Server, but the challenges persisted, including non-functional update and delete queries and slow performance.
About The Customer
Devsisters is a global entertainment and gaming app developer known for its popular game, Cookie Run. Launched in 2013, Cookie Run quickly gained popularity, recording 2.9 million daily active users in Korea and over 10 million downloads within 12 weeks of its launch. Today, the franchise boasts 200 million global users. Devsisters runs the game on Amazon Web Services (AWS) and uses Apache Spark to analyze customer profile and in-game activity data to make better product decisions. The company has a data-driven culture, with analytics being critical to its mission of providing the best experience to players around the world with superior technology, service, and content.
The Solution
To address its challenges, Devsisters implemented the Databricks Lakehouse Platform on AWS, which stores, understands, and analyzes all data types for downstream analytics and machine learning. This solution allowed Devsisters to shift its focus from infrastructure maintenance to leveraging insights derived from players' data. The Databricks Lakehouse Platform also includes integrated capabilities like Databricks SQL, a serverless data warehouse that companies can use to run SQL and BI applications at scale with increased performance, and Delta Lake, an open source storage layer that increases data lake reliability. Devsisters also tested Photon, the next-generation engine on the Databricks Lakehouse Platform that offers fast query performance at a lower cost. By using Databricks Lakehouse to support the data warehouse, Devsisters' data warehouse engineers redesigned and loaded logical data in the data lake for decision-making.
Operational Impact
  • The implementation of the Databricks Lakehouse Platform has significantly improved Devsisters' operational efficiency. With simplified data analysis and data warehouse management, data platform engineers can now focus on improving data platform capabilities and performance, rather than spending time on troubleshooting. The data team is innovating faster, with development time reduced by 30%. The company has also been able to leverage better data insights to create a statistical model that predicts the lifetime value derived from the entire game process. The Databricks Lakehouse provides an integrated data environment, combining the strengths of both a data lake and a data warehouse to enhance integration among indicators. This has enabled Devsisters to deliver powerful insights while providing reliable data.
Quantitative Benefit
  • 30% reduction in development time, accelerating new feature releases
  • 200% faster analysis of in-game activity, increasing gamer engagement
  • Ability to process and analyze more than 10TB of game player data every day

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