dbt is an open-source tool for transforming data, helping data engineers build and define a DAG of SQL queries called models.
Sound familiar? By adding dbt models as cells, the Count canvas becomes a natural place to collaboratively explore, visualise, and develop your dbt models.
Why import dbt metadata?
With a dbt integration your database tables in Count are annotated with dbt model and test metadata, allowing you to import raw and compiled code with lineage intact. Count compiles your raw code live, allowing you to swap model references for cells and vice-versa.
This simple but powerful feature enables lots of helpful workflows. For example, you may:
Import a database table with all upstream models in one click.
Explode a model into its component CTEs for better debugging and comprehension.
Execute models against other databases - for example, staging and production environments.
Prototype and iterate models live with the data team, then export dbt-ready model files.
Getting started
Find video tutorials, example use cases and case studies in this canvas.