Databricks processes your data. dbt defines what it means

Databricks argues that choosing its platform for compute, storage, machine learning, and AI workloads should be considered separately from deciding where transformation logic and data definitions reside. The post contends that placing transformations in Databricks-native tools such as Lakeflow pipelines and notebooks can increase migration costs and make auditing, version control, and metric governance more dependent on platform access, although it acknowledges Unity Catalog’s lineage and governance capabilities. It presents dbt as a complementary transformation layer that stores SQL models, tests, contracts, semantic definitions, and lineage in version-controlled, portable code capable of running across Databricks and other data platforms. The author highlights dbt’s open-source Fusion runtime, cross-platform support, Semantic Layer, community adoption, and state-based incremental builds as reasons organizations may retain independence while using Databricks infrastructure. It recommends that executives assess workload portability, audit access, ownership of metric definitions, and contingency plans before consolidating transformation tooling within a single vendor.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.