airflow-dbt VS dbt-fal

Happy data scientists use fal and dbt

fal (https://github.com/fal-ai/fal) helps with this! In fact we wrote a blog post about feature engineering with fal and dbt recently

We built a dbt adapter that helps you run local Python code with your dbt project with any other data warehouse. You can see it here: https://github.com/fal-ai/fal/tree/main/adapter

This new adapter helps you run your dbt Python models with isolated Python environments using our open source library: https://github.com/fal-ai/isolate

Have you considered fal? https://github.com/fal-ai/fal

Something to maybe comment on the post is that dbt is introducing Python transformations on the data warehouse offering (e.g. Snowspark) soon and that there are tools like fal that enable these Python transformations to run in a different environment which you have control over.

Fal-AI ( https://github.com/fal-ai/fal ) Fal helps to run Python scripts directly from the dbt project. For example, you can load dbt models directly into the Python context which helps to apply Data Science libraries like SKlearn and Prophet in the dbt models. This especially improves the data science capabilities within a data pipeline. What I extremely like about fal is that it extends dbt from a interesting angle.

- 🐍 fal ai: Fal helps to run Python scripts directly from the dbt project. For example you can load dbt models directly into the Python context which helps to apply Data Science libaries like SKlearn and Prophet in the dbt models.

I haven’t tried it yet but I know https://fal.ai/ helps you run python alongside dbt.

Yes I agree with you that having fivetran/airbyte and dbt covers a lot of the airflow use cases.. That being said you might still want to run some scripts after the DBT transformation is over, we ran into this exact problem and built a useful CLI tool for running python scripts alongside the dbt run.

Great write-up! For your logging integration, you might have a look at fal. There's an example of sending events to Datadog