Why Orchestrating dbt with Airflow? - Purpose & Use Cases

The Big Idea

What if your data transformations could run themselves perfectly every day without you lifting a finger?

The Scenario

Imagine you have many data transformation tasks to run every day. You try to run each dbt model one by one manually, opening your terminal, typing commands, and waiting for each to finish before starting the next.

Sometimes you forget the order, or miss a step, and your data ends up messy or incomplete.

The Problem

Doing this by hand is slow and tiring. It's easy to make mistakes like running models in the wrong order or forgetting to run some models at all.

Also, if something fails, you have to find out yourself and fix it, which wastes time and can delay important reports.

The Solution

Using Airflow to orchestrate dbt means you automate the running of all your dbt models in the right order.

Airflow watches over the process, runs tasks when they should start, and alerts you if something goes wrong.

This way, your data pipeline runs smoothly every day without you needing to babysit it.

Before vs After

✗ Before

dbt run --models model_a
dbt run --models model_b
dbt run --models model_c

✓ After

from airflow import DAG
from airflow_dbt.operators.dbt import DbtRunOperator
from datetime import datetime

with DAG('dbt_workflow', start_date=datetime(2023, 1, 1), schedule_interval='@daily', catchup=False) as dag:
    run_models = DbtRunOperator(task_id='run_dbt_models')

What It Enables

You can build reliable, automated data pipelines that run complex dbt transformations on schedule without manual effort.

Real Life Example

A company needs fresh sales reports every morning. By orchestrating dbt with Airflow, their data team ensures all transformations run overnight automatically, so reports are ready on time without anyone staying late.

Key Takeaways

Manual dbt runs are slow and error-prone.

Airflow automates and manages dbt tasks reliably.

This saves time and ensures data pipelines run smoothly every day.