JamCity

Customer Case Study

JamCity

Jam City is a global leader in mobile entertainment. They are the creative powerhouse behind award winning franchises Cookie Jam and Panda Pop, and the go-to studio for Hollywood, having developed games for iconic brands including Harry Potter, Family Guy and Marvel Avengers.

Industry

Gaming

Vertical Use Case

  • Recommendation Engine: Delivering a more personalized gaming experience.

Technical Use Case

  • Ingest and ETL
  • Machine Learning

The Challenges

Jam City collects massive volumes of mobile gaming data reaching hundreds of thousands of records per second. Teams need to analyze this data to learn how players engage with their games and improve the  gaming experience. Unfortunately, legacy data architectures presented a number of hurdles:

  • Inability to Scale Data Warehouse: Traditional data warehouse architecture that didn’t scale, was difficult to add capacity, and slow performance with 24hr ETL cycles.
  • Setting up and maintaining compute infrastructure required large amounts of time and resources.
  • Limited Machine Learning: A data warehouse is not a great source for doing iterative machine learning models.

The Solution

Databricks has provided Jam City with a unified analytics platform to democratize data across their organization to accelerate downstream innovation that improves the gaming experience.

  • Fully Managed Platform: A fully managed cloud platform simplifies operations and delivers superior performance of ETL pipelines at scale.
  • Automated Infrastructure Management: Simplified cluster management with auto-scaling significantly reduced time spent on data engineering.
  • Improved Data Performance and Reliability: Delta Lake has solved several data engineering problems such as evolving schemas, replaying of data with errors, representing data in Parquet files, etc.
  • Interactive Workspace: Data scientists can collaborate, share, and track data and insights across various programming languages, fostering an environment of transparency and improving productivity.

The Results

  • Faster Time to Market: Able to build reliable ETL pipelines that perform much faster – From 24 hours ETL jobs to only a few minutes. This has revolutionized data driven decision making – questions that took days to answer can now be answered in minutes.
  • Improved Data Science Productivity: Collaborative notebooks accelerated data science productivity by 15%. Teams can now build and deploy models significantly faster leading to highly personalized and engaging gaming experiences.

With Databricks, we're able to take all of our data and the power to get value from that data and put it in the hands of all our employees, and that's a game changer for us.

Eric Wasserman — Senior Architect, Jam City