DBT handles dependencies and data lineage by providing a set of features that allow users to manage and organize data models, and track the relationships between them.
- Dependency Management: DBT provides a way to manage dependencies between data models. This is important in a data warehouse environment where data models are built on top of each other. DBT uses a directed acyclic graph (DAG) to define dependencies between data models. This will allows users to easily understand the relationships between models, and to ensure that models are built in the correct order.
- Data Lineage: DBT provides a way to track the lineage of data models. This is useful for understanding how data flows through a data warehouse. DBT can automatically track the lineage of data models. This will allow users to trace data from source to destination, and to understand how data is transformed along the way.
- Visualization: DBT provides visualization of the data lineage and dependencies. This will allow users to easily understand the relationships between models, and to ensure that models are built in the correct order.
- Documentation: DBT also provides a way to document the data models. This will allow users to easily understand the purpose and usage of a model, and to trace the source of the data and the calculation performed on it.
DBT handles dependencies and data lineage by providing a set of features such as dependency management, data lineage tracking, visualization and documentation. These features allow users to manage and organize data models. This also allow you to understand the relationships between them. This will make it easier to understand how data flows through a data warehouse and how data is transformed along the way.
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