The Catalog Linked Database Diaries: On Freshness and Writes​

Last November, at dbt Summit, Jeremy introduced dbt’s multi-platform Iceberg capabilities.

What intrigued us most was the promised interconnectivity of Databricks Unity Catalog and Snowflake catalog-linked databases.

AI’s all the rage, but another little revolution is taking shape: Teams are breaking their data storage out of vendor-specific platforms. For months, we have been chatting with users excited to adopt Iceberg as a core pillar of their data architecture. The Iceberg table format and Iceberg REST catalogs are the emerging standards powering that flexibility.

For dbt’s part, this shows up in two concrete use cases:

• dbt projects at scale: Teams share one logical database, with many schemas and hundreds to thousands of tables
• Cross-platform mesh: One project in Snowflake, one in Databricks, sharing data without juggling manual refreshes or metadata pointers