Personalized predictions can be created by analyzing user clickstream data and using vector embeddings to capture the essence of an entity across multiple dimensions. This establishes relationships between users and items, revealing preferences and interests. BigQuery facilitates batch processing of vector embeddings, which are then fed into Spanner for efficient retrieval of these relationships via vector search. This enables real-time personalized recommendations with sub-ms response times. This solution offers accuracy, scalability, and real-time responsiveness.