Databricks and Cube are used together to build a unified semantic layer for organizations, making cloud data accessible and consistent to all data consumers. A semantic layer acts as middleware between data sources and downstream applications, abstracting physical data models and providing improved field labeling and dynamically calculated metrics. The combination of Databricks' Lakehouse with Cube's data modeling, access control, caching, and APIs enables secure delivery of consistent metrics to business intelligence tools and AI agents. By unifying the data stack, Cube and Databricks empower data engineers to handle data models through code, fostering collaborative efforts and streamlined code reviews. The integration of Cube with Databricks allows for seamless queries without data extraction, optimized performance by leveraging computing power, and pre-aggregations that reduce repetitive queries on the Databricks instance. This enables users to build any application they can dream of using first-class APIs such as SQL, REST, and GraphQL.