How does dbt assist with data lineage?

dbt assists with data lineage primarily by building a dependency graph of models. This dependency graph illustrates how different models connect to one another, showing the flow of data from sources through various transformations to the final output. By mapping out these relationships, dbt allows analysts and engineers to understand where data originates, how it transforms, and where it ultimately resides within the analytics ecosystem. This capability is crucial for improving data traceability, ensuring data quality, and facilitating debugging—enhancing overall data governance within an organization.

In contrast, visualization dashboards focus on presenting data insights rather than detailing the underlying connections between models. Storing all historical data snapshots addresses data retention and analysis over time but does not directly give information about how models are linked. Similarly, providing real-time data tracking is more about monitoring data as it moves rather than mapping its historical lineage or transformations. Therefore, the ability to build a dependency graph specifically highlights dbt's strength in data lineage.