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

Hey everyone!

Just wanted to drop in and share some news: as of April 2024, we’re saying goodbye to dbt-fal. Yep, it’s been quite the ride, but we’re switching gears to pour all our energy into something super exciting – creating the first-ever generative media platform for developers over at fal.ai! 🚀 We’re all in on this and can’t wait to see where it takes us.

Big thanks to every single one of you who’s been with us on the dbt-fal adventure. Your support and contributions mean the world. We’ve done some awesome stuff together, and this isn’t the end. Just a new chapter. So, here’s to more amazing things ahead, and we’re stoked to have you join us for the ride.

Cheers!

• No Further Development: The project will no longer receive updates or new features.
• Security Vulnerabilities: We will not be addressing new security vulnerabilities after April 8, 2024. We advise users to consider this when deciding to continue the use of the project.
• Archival: The repository will be archived, making it read-only. While the code will remain accessible for educational and historical purposes, we encourage users to fork the repository if they wish to continue development on their own.

Yes, the project will remain available for use, but please be aware that no new updates or security patches will be provided moving forward.

Unfortunately, none that we are aware of.

If you want to talk about dbt Python support, the best place to do so is the dbt Slack community. For other questions, feel free to reach out to [email protected]

We want to take a moment to thank everyone who contributed to dbt-fal, from our amazing contributors and users to anyone who spread the word about our project. Your support was invaluable.

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