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 long to list here. (For a comprehensive overview, go here)

dbt's SQL only design is powerful, but whenever we wanted to get out of SQL-land and connect to external services or get into Python-land for any reason, we had a hard time doing so without having to resort to a heavier weight orchestration tool such as Airflow.

Today, we wanted to share with you an open-source library that we have been working on called fal, which we built to enable Python workloads (sending alerts to Slack, building predictive models, pushing data to non-data warehouse destinations and more) right within dbt in a simple way.

Some examples of what you can do with fal:

• download dbt models using a familiar ref syntax as pandas dataframes
• send slack messages on model runs
• do forecasts on metrics
• do sentiment analysis on support tickets

Here is a video of it in action.

For a full documentation of what is possible, check out our README.

We hope you find fal useful in your day to day data work. We will be posting many how-to guides over the next days.

Don't forget to give us a ⭐ on Github:

You can also join us here on our Discord community to give us feedback or ask us any questions.