Airflow vs Bruin
Complete Comparison
An honest, in-depth comparison between Apache Airflow and Bruin. Understand the differences between a pure orchestrator and a complete data platform.
| Feature | Airflow | Bruin |
|---|---|---|
| Orchestration | ||
| Data Ingestion | ||
| SQL Transformations | ||
| Python Support | DAGs only | Full support |
| Data Quality Checks | ||
| Configuration | Python DAGs | YAML |
| Learning Curve | Steep | Easy |
| Setup Complexity | Complex | Simple |
| Open Source | ||
| Managed Cloud Option | Astronomer | |
| Infrastructure Costs | High | Low |
| Deployment Options | Self-hosted | Anywhere |
Core Philosophy
Orchestrator vs Complete Platform
Apache Airflow
Airflow is a pure workflow orchestrator—it schedules and monitors tasks but doesn't provide data ingestion, transformation, or quality features.
- Powerful orchestration capabilities
- Python-based DAG definitions
- Steep learning curve
- Complex setup and configuration
- No data ingestion built-in
- No transformation framework
- No built-in quality checks
You need separate tools: Fivetran/Airbyte for ingestion, dbt for transformation, custom scripts for quality
Bruin
Bruin is a complete data platform—orchestration plus ingestion, transformation, and quality in one unified tool.
- Built-in orchestration with dependencies
- Data ingestion from 100+ sources
- SQL & Python transformations
- Built-in quality checks
- Simple YAML configs (no Python DAGs)
- Easy to learn and set up
Everything in one: Replaces Airflow + Fivetran/Airbyte + dbt + quality tools
Complexity
Python DAGs vs Simple SQL
Airflow requires writing complex Python DAGs to orchestrate SQL queries. Bruin lets you write simple SQL files with minimal metadata.
Airflow DAG
Complex Python code to orchestrate 2 SQL queries:
from airflow import DAG
from airflow.providers.snowflake.operators.snowflake import SnowflakeOperator
from datetime import datetime, timedelta
default_args = {
'owner': 'data-team',
'depends_on_past': False,
'start_date': datetime(2024, 1, 1),
'email_on_failure': True,
'email_on_retry': False,
'retries': 2,
'retry_delay': timedelta(minutes=5),
}
dag = DAG(
'snowflake_orders_pipeline',
default_args=default_args,
description='Process orders in Snowflake',
schedule_interval='0 2 * * *',
catchup=False,
)
# First SQL query - staging
staging_orders = SnowflakeOperator(
task_id='create_staging_orders',
snowflake_conn_id='snowflake_default',
sql="""
CREATE OR REPLACE TABLE staging.orders AS
SELECT
order_id,
customer_id,
order_date,
total_amount,
status
FROM raw.orders
WHERE order_date >= CURRENT_DATE - 7
""",
warehouse='COMPUTE_WH',
database='ANALYTICS',
schema='STAGING',
dag=dag,
)
# Second SQL query - analytics
analytics_orders = SnowflakeOperator(
task_id='create_analytics_orders',
snowflake_conn_id='snowflake_default',
sql="""
CREATE OR REPLACE TABLE analytics.orders AS
SELECT
o.order_id,
o.customer_id,
o.order_date,
o.total_amount,
c.customer_name,
c.customer_email
FROM staging.orders o
LEFT JOIN raw.customers c
ON o.customer_id = c.customer_id
""",
warehouse='COMPUTE_WH',
database='ANALYTICS',
schema='ANALYTICS',
dag=dag,
)
# Set up dependencies
staging_orders >> analytics_orders80+ lines of Python boilerplate, complex setup, requires understanding of Airflow operators, connections, and dependencies
Simple SQL file with minimal metadata:
/* @bruin
name: staging.orders
materialization:
type: table
@bruin */
SELECT
order_id,
customer_id,
order_date,
total_amount,
status
FROM raw.orders
WHERE order_date >= CURRENT_DATE - 7Second asset with automatic dependency:
/* @bruin
name: analytics.orders
materialization:
type: table
depends:
- staging.orders
@bruin */
SELECT
o.order_id,
o.customer_id,
o.order_date,
o.total_amount,
c.customer_name,
c.customer_email
FROM staging.orders o
LEFT JOIN raw.customers c
ON o.customer_id = c.customer_idJust SQL + simple metadata. No Python, no operators, no complex configuration. Dependencies are automatically inferred.
Decision Guide
When to Choose Each Tool
Choose Airflow if...
You only need orchestration
Already have ingestion, transformation, and quality tools in place.
You have Python expertise
Comfortable writing and maintaining complex Python DAGs.
You're okay with complexity
Have resources to manage infrastructure and multiple tools.
Choose Bruin if...
You want a complete platform
Need ingestion + transformation + quality + orchestration in one unified tool.
Simplicity matters
Prefer simple YAML configs over complex Python DAGs.
Simplicity is important
Prefer a unified platform with simple configuration over managing multiple tool subscriptions.
Faster time to value
Want to build and deploy pipelines quickly without weeks of setup.
You want deployment flexibility
Deploy anywhere: local, GitHub Actions, EC2, or fully managed cloud.
Ready to Simplify Your Stack?
Replace Airflow + Fivetran/Airbyte + dbt + quality tools with one unified platform.