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Quickstart

Let's discover fal in less than 5 minutes.

fal allows you to run Python scripts directly from your dbt project.

With fal, you can:

  • Download dbt models into a Python context with a familiar syntax: ref('my_dbt_model').
  • Send Slack notifications upon dbt model success or failure.
  • Use Python libraries such as sklearn or prophet to build more complex pipelines downstream of dbt models.

and more...

1. Install fal​

$ pip install fal

2. Go to your dbt directory​

$ cd ~/src/my_dbt_project

3. Create a Python script: send_slack_message.py​

import os
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError

CHANNEL_ID = os.getenv("SLACK_CHANNEL")
SLACK_TOKEN = os.getenv("SLACK_BOT_TOKEN")

client = WebClient(token=SLACK_TOKEN)

message_text = f"Model: {context.current_model.name}. Status: {context.current_model.status}."

if str(context.current_model.status) == 'success':
  # Read model as pandas.DataFrame
  df = ref(context.current_model.name)
  message_text += f" Size: {df.size}."

try:
    response = client.chat_postMessage(
        channel=CHANNEL_ID,
        text=message_text
    )
except SlackApiError as e:
    assert e.response["error"]

As you can see from the context object, fal makes certain variables (and functions) avaliable in this script. Check out the fal scripts section for more details

4. Add a meta section in your schema.yml​

models:
  - name: some_model
    meta:
      fal:
        scripts:
          - send_slack_message.py

5. Run fal flow run​

This command manages your dbt runs for you, by running scripts and models in the correct order.

$ fal flow run
# 1. dbt model `some_model` is run
# 2. slack message is sent with the run result