do more with dbt. fal helps you run Python alongside dbt, so you can send Slack alerts, detect anomalies and build machine learning models.

fal-ai Last update: Jun 13, 2023

fal: do more with dbt

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

Let's discover fal in less than 5 minutes:

Introduction

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

`fal` – 📖 README

With the fal 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.

Check out the fal README for more information.

For more details on fal, go to the documentation!

`dbt-fal` – 📖 README

With the dbt-fal 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.

Check out the dbt-fal README for more information.

For more details on dbt-fal, go to the documentation!

Why are we building this?

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

dbt's SQL only design is powerful, but if you ever want to get out of SQL-land and connect to external services or get into Python-land for any reason, you will have a hard time. We built fal to enable Python workloads (sending alerts to Slack, building predictive models, pushing data to non-data-warehouse destinations and more) right within dbt.

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.