---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: a42fb59d27120d03bedef80f6e9cedfc68d48edee375da9227077ec5e6373420
  pageDirectory: concepts
  sources:
    - use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - development-and-production-workflow-for-dbt-on-databricks
    - Production Workflow for dbt on Databricks and Development
    - DAPWFDOD
  citations:
    - file: use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md
title: Development and Production Workflow for dbt on Databricks
description: Recommended workflow of developing dbt projects against a Databricks SQL warehouse for testing and debugging, then running in production via the dbt task in a Databricks job.
tags:
  - dbt
  - databricks
  - development
  - production
timestamp: "2026-06-19T23:21:33.941Z"
---

# Development and Production Workflow for dbt on Databricks

The **Development and Production Workflow for dbt on Databricks** describes the recommended approach for building and deploying dbt Core projects using Databricks infrastructure. This workflow separates the development environment — where models are iteratively built and tested — from the production environment — where transformations are scheduled, monitored, and executed at scale.

## Development Environment

Databricks recommends developing dbt projects against a Databricks SQL warehouse. Using a SQL warehouse during development allows you to test the SQL generated by dbt and leverage the SQL warehouse query history to debug the queries produced by dbt. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]

## Production Environment

To run dbt transformations in production, Databricks recommends using the **dbt task** in a Databricks job. By default, the dbt task runs the dbt Python process using Databricks compute and executes the dbt-generated SQL against the selected SQL warehouse. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]

Running dbt as a job task provides several benefits:

- **Automation**: Schedule dbt tasks and orchestrate workflows that include dbt transformations. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]
- **Monitoring**: Monitor dbt transformations and receive notifications on their status. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]
- **Workflow integration**: Include dbt projects in workflows with other tasks — for example, ingest data with Auto Loader, transform with dbt, and analyze with a notebook task. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]
- **Artifact archiving**: Automatic archiving of artifacts from job runs, including logs, results, manifests, and configuration. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]

## Supported Compute Targets

You can run dbt transformations on a serverless SQL warehouse, pro SQL warehouse, Databricks compute, or any other dbt-supported warehouse. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]

If your workspace is [Unity Catalog](/concepts/unity-catalog.md)-enabled and Serverless Jobs is enabled, the job runs on Serverless compute by default. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]

## Important Considerations

Developing dbt models against a SQL warehouse and running them in production on Databricks compute can lead to subtle differences in performance and SQL language support. Databricks recommends using the same Databricks Runtime version for both the compute and the SQL warehouse to minimize these discrepancies. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]

## Requirements

- Use the [dbt-databricks](/concepts/dbt-databricks-adapter-package.md) package (not the dbt-spark package), which is a fork of dbt-spark optimized for Databricks. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]
- Set up [Databricks Git folders](/concepts/databricks-git-folders-for-cicd.md) to use dbt projects in a Databricks job — you cannot run a dbt project from DBFS. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]
- Have serverless or pro SQL warehouses enabled. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]
- Have the Databricks SQL entitlement. ^[use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md]

## Related Concepts

- dbt Core
- [dbt-databricks package](/concepts/dbt-databricks-adapter-package.md)
- [Databricks Jobs](/concepts/dbt-task-in-databricks-jobs.md)
- Databricks SQL warehouses
- [Databricks Git folders](/concepts/databricks-git-folders-for-cicd.md)
- [Unity Catalog](/concepts/unity-catalog.md)
- Serverless Jobs

## Sources

- use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md

# Citations

1. [use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws.md](/references/use-dbt-transformations-in-lakeflow-jobs-databricks-on-aws-9420061c.md)
