DBT : How does DBT handle versioning of data models?

DBT does not inherently include versioning capabilities for data models, but it can be integrated with version control systems like Git to manage versioning.

When using DBT with Git or another version control system, you can take advantage of versioning features, such as:

  1. Commit history: Every change made to the data models (i.e., SQL files and configurations) can be tracked with commit messages, allowing you to maintain a detailed history of changes and enabling easy rollback to previous versions when needed.
  2. Branching: Branches can be used to develop and test new features or changes to the data models without impacting the main codebase. Once the changes are tested and validated, the branch can be merged back into the main branch.
  3. Collaboration: Version control systems provide a platform for multiple team members to work on the same data models simultaneously, with the ability to resolve conflicts and merge changes when necessary.
  4. Release management: You can tag specific commits as releases, making it easy to deploy specific versions of your data models to different environments, like staging or production.

To make the most of versioning capabilities, it’s essential to follow best practices like writing clear commit messages, using descriptive branch names, and keeping the main branch stable and production-ready.

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