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Use case
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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.

Use cases:
Gaming
Created by:
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Quix
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A Quix Cloud pipeline demonstrating how to create a real-time a player engagement pipeline.

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/

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