Introducing dbt Integration

Cube has introduced a new integration with dbt that allows existing dbt projects to connect with Cube, transforming each dbt model into a governed cube within the semantic layer, which includes dimensions, measures, descriptions, and joins derived from previous modeling efforts. This integration operates by reading dbt models and converting them into foundational cubes in the Cube data model, enabling the creation and extension of these cubes with advanced metrics. Users can manually trigger a pull or automate it via CI pipelines or repository pushes, with Cube regenerating the corresponding cubes and submitting them for review before they go live. The integration maintains dbt as the source of truth for table transformations, while Cube manages the models built on top, facilitating dashboards, embedded analytics, and AI queries without altering the dbt repository. The integration supports a one-way metrics sync from dbt to Cube and is designed to work with various data warehouses like Snowflake, Amazon Redshift, PostgreSQL, and Google BigQuery. The setup involves connecting Cube to the repository via HTTPS or SSH with encrypted credentials, and the process ensures that production data remains untouched by operating in a sandbox environment.

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