dbt is a data transformation tool that brings software engineering best practices to data workflows. It notably allows you to define data pipelines as a set of models for which you can define data quality tests.

Why integrate Sifflet with dbt?

By integrating Sifflet with dbt, you'll benefit from the following capabilities:

  • Enriching the dataset entries in the Sifflet data catalog with dbt execution metadata. For every dbt-generated dataset, Sifflet will provide the following metadata:

    • Last Execution Timestamp: See exactly when the corresponding dbt model was last run.
    • Last Execution Status: Quickly identify if the latest execution was successful, failed, or skipped.
    Dataset catalog entry with dbt metadata

    Dataset catalog entry with dbt metadata

  • Centralizing all your dbt test run results in the Sifflet monitoring catalog. This allows you to leverage all the benefits of Sifflet monitors (including alerting) on top of dbt tests and gives you a comprehensive understanding of the status of past tests:

Sifflet dbt tests library

dbt tests in Sifflet

  • Accessing dbt metadata in Sifflet as part of the asset page. Once Sifflet connects a dataset to its dbt model, a dedicated dbt tab will be added to the asset page with various metadata retrieved from dbt:
The dbt tab

The dbt tab

  • Enriching the lineage graph with dbt execution metadata. For every dbt-generated dataset, Sifflet will show the dbt run status in the lineage:
The lineage graph with dbt metadata

The lineage graph with dbt metadata

Integrate your dbt project(s) with Sifflet