Real-time recommendations for mobile phones using data streaming

This post details the implementation of a real-time recommendation system for mobile phones using Confluent Cloud. It addresses the challenges of scaling personalization for millions of users and the limitations of traditional approaches. The architecture leverages Confluent Cloud for aggregating content, serving personalized recommendations, and tracking user interactions. Key technical contributions include using CDC source connectors to stream user data from Postgres to Kafka, creating ksqlDB materialized views for real-time user enrichment, and utilizing fanout for efficient data consumption by recommendation and reporting services. It also highlights the use of Stream Lineage for debugging data flow issues and improving operational visibility.