Dbt vs Airflow: Key Differences and When to Use Each

dbt is a tool focused on transforming data inside your warehouse using SQL, while Airflow is a workflow orchestrator that schedules and manages complex data pipelines. Use dbt for data modeling and transformations, and Airflow to automate and coordinate tasks across 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 across systems
Language	SQL with Jinja templating	Python DAGs (Directed Acyclic Graphs)
Execution	Runs SQL queries in the warehouse	Runs and schedules arbitrary tasks/scripts
Complexity	Simpler, focused on SQL transformations	More complex, handles dependencies and retries
Use Case	Building clean, tested data models	Coordinating multi-step pipelines
Monitoring	Basic logs and test results	Advanced monitoring, retries, alerts

Key Differences

dbt is designed specifically for transforming data already loaded into a data warehouse. It uses SQL with Jinja templating to build modular, tested data models. Its main focus is on writing clean, maintainable SQL code that runs inside the warehouse, making it easy to version control and test data transformations.

On the other hand, Airflow is a general-purpose workflow orchestrator written in Python. It manages and schedules complex pipelines that can include running scripts, moving data, triggering jobs, and more. Airflow handles task dependencies, retries, and alerts, making it suitable for coordinating end-to-end data workflows beyond just SQL transformations.

While dbt focuses on the transformation layer, Airflow focuses on orchestration. They often complement each other: Airflow can schedule and trigger dbt runs as part of larger pipelines.

Code Comparison

Here is an example of how dbt defines a simple transformation model to select and clean data.

sql

/* models/clean_customers.sql */

select
  id,
  upper(first_name) as first_name,
  upper(last_name) as last_name,
  email
from raw.customers
where email is not null

Output

A table named clean_customers with cleaned customer data having uppercase names and non-null emails.

Airflow Equivalent

This Airflow DAG runs a SQL transformation task similar to the dbt model above by executing a SQL query on a database.

python

from airflow import DAG
from airflow.providers.postgres.operators.postgres import PostgresOperator
from datetime import datetime

with DAG('clean_customers_dag', start_date=datetime(2024, 1, 1), schedule_interval='@daily', catchup=False) as dag:
    clean_customers = PostgresOperator(
        task_id='clean_customers_task',
        postgres_conn_id='my_postgres',
        sql="""
        CREATE TABLE IF NOT EXISTS clean_customers AS
        SELECT
          id,
          UPPER(first_name) AS first_name,
          UPPER(last_name) AS last_name,
          email
        FROM raw.customers
        WHERE email IS NOT NULL;
        """
    )

Output

Airflow schedules and runs the SQL task daily, creating the clean_customers table in the database if it does not exist.

When to Use Which

Choose dbt when you want to focus on building, testing, and maintaining SQL-based data transformations inside your warehouse with version control and modularity.

Choose Airflow when you need to orchestrate complex workflows that include multiple steps, dependencies, and different types of tasks beyond SQL, such as data ingestion, machine learning jobs, or notifications.

Often, teams use both: Airflow to schedule and manage pipelines, and dbt to handle the transformation logic within those pipelines.

Key Takeaways

dbt specializes in SQL data transformations inside warehouses with testing and modularity.

Airflow is a flexible workflow orchestrator for scheduling and managing complex pipelines.

Use dbt for clean data modeling and Airflow for coordinating multi-step workflows.

They complement each other and are often used together in modern data stacks.

Choose based on whether your focus is transformation (dbt) or orchestration (Airflow).