Announcing fal Launch Week #2 We are excited to bring you our second Launch Week! Over the past couple of months we have been working on exciting new features to make running Python even more delightful. Similar to the previous launch week, each day of the week we are going to announce a new feature
Python or SQL? Why not both? Python vs SQL is a frequent debate in the data community and it usually revolves around which language is better for data transformations. At Features and Labels, we think about it a little differently. In our opinion, today, all transformations that are supported by SQL should be done in SQL.
Introducing fal flow We started the fal project with a simple objective; we wanted to build a developer tool that enables running python scripts downstream of dbt models. Since we launched the initial capabilities of fal, we have received an overwhelming amount of requests to run python scripts before, after and in between
Announcing fal Launch Week #1 After our Unbundling of Airflow article, it has been a wild few weeks here at Features & Labels. Gorkem’s now-viral post resonated with tens of thousands of data people around the globe. Meanwhile, we have been hard at work cooking up the future of our open-source library fal, which
How-to Going from dbt models to pandas DataFrames One of our favorite features of dbt is the ability to build dependencies between data models using the ref function. With ref, you can build pipelines all in SQL, simply by referring to other tables in your select statements. But what if you want to refer to these models in
Introducing fal: do more with dbt At Features & Labels, dbt is one of our favorite tools to use while managing our data pipelines. It is a category defining tool (see Analytics Engineering) that enables us to build data transformations without having to write boilerplate data engineering code + a ton of other benefits that are too
