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fal setup (Python models)

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Some core functionality may be limited. If you're interested in contributing, check out the source code for each repository listed below.

Overview of dbt-fal

  • Maintained by: fal.ai
  • Authors: Features & Labels (https://github.com/fal-ai)
  • GitHub repo: fal-ai/fal
  • PyPI package: dbt-fal
  • Slack channel: #tools-fal
  • Supported dbt Core version: v1.3.0 and newer
  • dbt Cloud support: Not Supported
  • Minimum data platform version: n/a

Installing dbt-fal

pip is the easiest way to install the adapter:

pip install dbt-fal[<sql-adapter>]

Installing dbt-fal will also install dbt-core and any other dependencies.

You must install the adapter for SQL transformations and data storage independently from dbt-fal.

Configuring dbt-fal

For fal-specifc configuration please refer to fal Configuration

For further info, refer to the GitHub repository: fal-ai/fal

Setting up fal with other adapter‚Äč

fal offers a Python runtime independent from what database you are using and integrates seamlessly with dbt. It works by downloading the data as a Pandas DataFrame, transforming it in a local Python runtime and uploading it to the database. The only configuration change you need to do is adding it to the profiles.yml and setting the db_profile property as the database profile you are already using.

It will run all the SQL dbt models with the main adapter you specified in your profiles.yml and all the Python models are executed by the fal adapter.

Example:

profiles.yml
jaffle_shop:
target: dev_with_fal
outputs:
dev_with_fal:
type: fal
db_profile: dev_pg # This points to your main adapter
dev_pg:
type: postgres
...
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