dbt-fal is the easiest way to run Python with your dbt project.

The dbt-fal ecosystem has two main components: The command line and the adapter.

With the CLI, you can:

• Send Slack notifications upon dbt model success or failure.
• Load data from external data sources before a model starts running.
• Download dbt models into a Python context with a familiar syntax: ref('my_dbt_model') using FalDbt
• Programatically access rich metadata about your dbt project.

With the Python adapter, you can:

• Enable a developer-friendly Python environment for most databases, including ones without dbt Python support such as Redshift, Postgres.
• Use Python libraries such as sklearn or prophet to build more complex dbt models including ML models.
• Easily manage your Python environments with isolate.
• Iterate on your Python models locally and then scale them out in the cloud.

We think dbt is great because it empowers data people to get more done with the tools that they are already familiar with.

This library will form the basis of our attempt to more comprehensively enable data science workloads downstream of dbt. And because having reliable data pipelines is the most important ingredient in building predictive analytics, we are building a library that integrates well with dbt.

• Join us in fal on Discord
• Join the dbt Community and go into our #tools-fal channel