Databricks makes the case for running dbt on its lakehouse platform by highlighting four integrated capabilities: open storage formats (Delta Lake and Apache Iceberg), unified orchestration via Lakeflow Jobs, centralized governance through Unity Catalog, and automated performance optimization. The post argues that fragmented data stacks force teams to manage multiple systems for storage, compute, governance, and orchestration, leading to duplicated data and operational overhead. On Databricks, dbt models materialize into open table formats accessible by any query engine, permissions persist across table rebuilds, column-level lineage traces data from ingestion through transformation, and cost tracking is tied to specific dbt runs. Over 2,900 customers are cited as already running dbt on Databricks.
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Run dbt on open foundations with zero vendor lock-inOrchestrate dbt pipelines end-to-end with Lakeflow JobsSort: