Player Engagement
This project is an example of using analytics for gaming to enhance player engagement and real-time personalization. The platform includes various services such as player engagement insights and game personalization, and data sinks for offline model training. It's designed to showcase how easy it is to implement real-time game personalization.
Main project components
User DB CDC
Subscribe to changes made to the User Database.
Telemetry API
Subscribe to telemetry data being emitted from your game servers.
Player Engagement Insights
Process the data to gain insights into player activity and habits.
Game Personalization
Use your ML models to personalize player experiences to aid satisfaction and retention.
Player Enrichment Sink
Sink all player data to storage for offline model training.
Apache Iceberg Sink
Sink data to Apache Iceberg for training, marketing and further insight discovery.
Technologies used
- Docker - https://www.docker.com/
- Kubernetes - https://kubernetes.io/
- Quix Streams - https://github.com/quixio/quix-streams
- Pandas - https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html
- Iceberg - https://iceberg.apache.org/