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Get the guideIn dbt-core, model access is a term that refers to the ability of users to designate certain models as either "private" or "public". Private models are exclusively accessible within a specific group, while public models can be referenced by other teams. This feature allows for better control and governance over data models within a dbt project.
# Example of designating a model as private
models: my_project_name: marts: customers: +group: customer_success
The above code snippet demonstrates how to apply a group label to a subdirectory in a dbt project, effectively making the model private. In this case, the 'customers' model is designated as part of the 'customer_success' group.
Applying a group label to a subdirectory in a dbt project is done in the dbt_project.yml file. The syntax is 'models: project_name: directory: subdirectory: +group: group_name'. This allows you to specify which group a model belongs to.
# Example of applying a group label
finance: +group: finance
In the code example, the 'finance' model is assigned to the 'finance' group. This means that only members of the 'finance' group can access this model.
Model contracts in dbt are used to define column names, data types, and constraints. This helps to avoid breaking changes for downstream queries. It's a form of model governance that ensures the integrity and reliability of your data models.
It's important to understand that they are a critical part of maintaining and managing your dbt models. They help ensure that changes to your models don't negatively impact downstream queries.
Managing model versions in dbt involves creating a new model version to provide a smoother upgrade path when a breaking change is unavoidable. This allows for better control over the evolution of your data models and minimizes disruption to dependent queries.
It's a crucial aspect of dbt model governance. It ensures that changes to your models are handled in a way that minimizes impact on dependent queries.
Model styling in dbt involves using underscores for naming models and pluralizing models. This is a convention that helps maintain consistency and readability in your dbt projects.
It's a convention that's widely followed in dbt projects. It helps ensure that your models are named in a consistent and readable manner.
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