Dbt vs Airflow: Key Differences and When to Use Each

dbt is a tool focused on transforming data inside a warehouse using SQL, while Airflow is a workflow orchestrator that schedules and manages complex data pipelines. dbt handles data modeling and testing, whereas Airflow manages task dependencies and execution order across various systems.

Quick Comparison

This table summarizes the main differences between dbt and Airflow across key factors.

Factor	dbt	Airflow
Primary Purpose	Data transformation and modeling inside data warehouses	Workflow orchestration and scheduling of tasks
Language	SQL with Jinja templating	Python
Focus	Transforming and testing data	Managing task dependencies and execution order
Scheduling	Triggered by commands or Airflow	Built-in scheduler for complex pipelines
Integration	Works inside warehouses like Snowflake, BigQuery	Integrates with many systems and tools
Use Case	Building clean, tested data models	Orchestrating multi-step data workflows

Key Differences

dbt is designed specifically for transforming raw data into clean, tested datasets inside a data warehouse. It uses SQL with Jinja templating to write modular, reusable transformations. dbt focuses on data modeling, testing, and documentation, making it easy to maintain and understand data transformations.

Airflow, on the other hand, is a general-purpose workflow orchestrator written in Python. It schedules and manages complex pipelines that can include data extraction, transformation, loading, and other tasks across different systems. Airflow handles task dependencies, retries, and monitoring but does not perform data transformations itself.

In practice, dbt often runs inside an Airflow pipeline as one step focused on data transformation, while Airflow manages the overall workflow including data ingestion, transformation, and downstream processes.

Code Comparison

Here is an example of how dbt defines a simple data transformation model using SQL.

sql

with source as (
    select * from raw.sales_data
),
filtered as (
    select * from source where sale_amount > 100
)

select
    customer_id,
    sum(sale_amount) as total_sales
from filtered
group by customer_id

Output

A table with customer_id and total_sales showing sum of sales over 100 per customer

Airflow Equivalent

This Python code shows an Airflow DAG that runs a dbt command as a task to execute the transformation above.

python

from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime

default_args = {
    'start_date': datetime(2024, 1, 1),
}

dag = DAG('dbt_run', default_args=default_args, schedule_interval='@daily')

dbt_task = BashOperator(
    task_id='run_dbt_model',
    bash_command='dbt run --models sales_transformation',
    dag=dag
)

Output

Airflow schedules and runs the dbt model named 'sales_transformation' daily

When to Use Which

Choose dbt when your main goal is to build, test, and document data transformations inside a data warehouse using SQL. It is perfect for analytics engineering and creating clean data models.

Choose Airflow when you need to orchestrate complex workflows that involve multiple steps, systems, or tools beyond just data transformation. It is ideal for scheduling, monitoring, and managing dependencies in data pipelines.

Often, using both together is best: Airflow manages the overall pipeline, and dbt handles the transformation step inside it.

Key Takeaways

dbt focuses on SQL-based data transformation and testing inside warehouses.

Airflow is a Python-based workflow orchestrator managing complex pipelines.

Use dbt for clean, maintainable data models and Airflow for scheduling and task dependencies.

They complement each other: Airflow runs dbt as part of larger workflows.

Choose tools based on whether you need transformation focus (dbt) or orchestration (Airflow).