What are Model Versions in dbt for Data Teams?
Model versions in dbt are a feature that allows data teams to support both current and future data source requirements without the need for managing separate tables and logic. This functionality enables teams to continue supporting existing data source consumers while simultaneously introducing new features for other consumers.
- Versioning scheme: The versioning scheme in dbt model versions is API-based, focusing on the major version only. This approach simplifies the versioning process and ensures compatibility across different versions.
- Version support: In dbt, each model can be versioned individually, providing flexibility and control over each data model.
- Latest version: By default, the latest version in dbt is the numerically greatest version. This feature ensures that the most recent and updated model is always used.
How do Model Versions Aid in Data Model Management?
Model versions play a crucial role in data model management and governance. They allow teams to track model updates and changes over time, providing a clear history of model evolution and modifications. This feature enhances transparency and accountability in data model management.
- Deprecation date: Older versions in dbt must specify a deprecation date. This requirement helps in managing and phasing out outdated models, ensuring the use of updated and relevant models.
- Model tracking: With model versions, teams can track the changes and updates in each model, providing a clear and comprehensive history of each model's evolution.
- Model governance: Model versions enhance governance by ensuring transparency and accountability in model updates and changes.
How are Model Versions Different from dbt_project.yml Versions?
Model versions and dbt_project.yml versions in dbt serve different purposes. While model versions refer to the versions of individual data models, dbt_project.yml versions refer to the compatibility of the dbt project with a specific version of dbt.
- dbt_project.yml versions: These versions indicate the compatibility of the dbt project with a specific version of dbt. They ensure that the project works seamlessly with the specified dbt version.
- Model versions: Model versions, on the other hand, refer to the versions of individual data models. They allow for versioning of each model separately, providing flexibility and control.
What are .yml Property File Versions in dbt?
.yml property file versions in dbt inform how dbt parses YAML files. These versions are different from model versions and dbt_project.yml versions, serving a specific purpose in the dbt environment.
- .yml property file versions: These versions guide how dbt parses YAML files. They ensure that the YAML files are correctly interpreted and processed by dbt.
- Role in dbt: .yml property file versions play a crucial role in the dbt environment, ensuring the correct parsing and processing of YAML files.
What are the Proposed Features for Model Versioning in dbt?
Several features have been proposed for model versioning in dbt. These include an API-based versioning scheme, individual model versioning, numerically greatest version as the latest version, and a requirement for older versions to specify a deprecation date.
- API-based versioning scheme: This proposed feature simplifies the versioning process and ensures compatibility across different versions.
- Individual model versioning: This feature provides flexibility and control over each data model, allowing for separate versioning of each model.
- Numerically greatest version as the latest version: This feature ensures that the most recent and updated model is always used.
- Deprecation date for older versions: This requirement helps in managing and phasing out outdated models, ensuring the use of updated and relevant models.
