Databricks delivers all four pillars natively integrated in one platform. When you run dbt on Databricks, you get the dbt developer experience on top of a lakehouse architecture designed for openness, governance, performance, and operational simplicity from day one. Let's look at how each of these works in practice:

Vendor lock-in is one of the most significant strategic risks to an organization’s data strategy. dbt is built with an open adapter framework, meaning your transformation logic isn't locked to any single platform. dbt is open by design, and Databricks provides an open platform to run it on. Many modern data stacks center on a proprietary storage layer that offers short-term convenience but introduces long-term friction. Over time, this leads to duplicated data and export pipelines to serve different consumers, storage formats that limit interoperability, and escalating switching costs as platform requirements evolve.

Databricks is an open lakehouse: a unified platform where your data lives in open table formats and is accessible through open interfaces, ensuring storage and governance aren’t tied to a single query engine. On Databricks, dbt models become tables in open formats, Delta Lake and Apache Iceberg, ensuring your transformed data remains accessible across the entire data landscape without exporting or maintaining parallel copies. This openness matters for dbt workflows specifically. Your carefully modeled silver and gold tables become reusable data products that downstream users can consume through any query engine, not just through the platform where dbt runs.