Imply Polaris introduces the use of upserts and Dimension Tables to enhance data management and analytics capabilities, focusing on data consistency and efficiency. Upserts in Polaris streamline database operations by combining insert and update actions, while Dimension Tables, a smaller version of upserts, store the latest snapshot of attribute data that evolves over time. These tables, which are integrated with event streaming pipelines like Kafka, allow for near-instant data updates and can be joined with event data for real-time analytics. Unlike traditional Lookup tables in Apache Druid, Dimension Tables simplify setup and maintenance, effectively leveraging JOINs to manage changing dimension values without the scalability issues associated with large datasets. The feature is already in use by companies such as MetaCX to achieve live identity normalization. The introduction of Dimension Tables marks the beginning of an Upsert roadmap, with future enhancements planned to support increased event throughput and comprehensive CDC input sources, aiming to enable analytics at a larger scale by facilitating the joining of decorative metadata with event transactions.