dbt

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.

  

• 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:

• 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:

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

Integrate your dbt project(s) with Sifflet

• dbt Cloud integration guide
• dbt Core integration guide