Apache Airflow
The leading workflow orchestration platform for data pipelines. Airflow lets you define, schedule, and monitor complex DAG-based pipelines in Python. The standard for data engineers and ML pipeline orchestration.
Why Apache Airflow?
You need to schedule and monitor complex multi-step data pipelines
Your pipelines have dependencies that need DAG-based orchestration
You want a rich UI for visualizing pipeline runs and debugging failures
Signal Breakdown
What drives the Trust Score
Download Trend
Last 12 months
Tradeoffs & Caveats
Know before you commitYou need real-time streaming — Airflow is batch-oriented
You want a simpler alternative (Prefect or Dagster have better DX)
Your team can't manage the Airflow infrastructure (use Cloud Composer or Astronomer)
Pricing
Free tier & paid plans
Open-source self-host free · Astronomer: $0 dev
Astronomer Cloud: $399/mo hosted
MWAA (AWS): ~$0.49/hr environment
Alternative Tools
Other options worth considering
Often Used Together
Complementary tools that pair well with Apache Airflow
Learning Resources
Docs, videos, tutorials, and courses
Get Started
Repository and installation options
View on GitHub
github.com/apache/airflow
pip install apache-airflowdocker run -p 8080:8080 apache/airflowQuick Start
Copy and adapt to get going fast
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
with DAG('etl_pipeline',
start_date=datetime(2024, 1, 1),
schedule_interval='@daily',
catchup=False) as dag:
extract = PythonOperator(task_id='extract', python_callable=fetch_from_source)
transform = PythonOperator(task_id='transform', python_callable=clean_and_model)
load = PythonOperator(task_id='load', python_callable=write_to_warehouse)
extract >> transform >> loadCode Examples
Common usage patterns
BashOperator and branching
Run shell commands and branch based on conditions
from airflow.operators.bash import BashOperator
from airflow.operators.python import BranchPythonOperator
def choose_branch(**context):
if context['ds'] == '2024-01-01':
return 'full_load'
return 'incremental_load'
branch = BranchPythonOperator(task_id='branch', python_callable=choose_branch)
full = BashOperator(task_id='full_load', bash_command='python load_full.py')
incr = BashOperator(task_id='incremental_load', bash_command='python load_incr.py {{ ds }}')
branch >> [full, incr]TaskFlow API (modern pattern)
Use @task decorator for cleaner DAG authoring
from airflow.decorators import dag, task
from datetime import datetime
@dag(schedule='@daily', start_date=datetime(2024, 1, 1), catchup=False)
def my_pipeline():
@task
def extract() -> list:
return fetch_data()
@task
def transform(raw: list) -> list:
return clean(raw)
@task
def load(data: list):
write_to_warehouse(data)
load(transform(extract()))
my_pipeline()Trigger DAG via REST API
Start a DAG run programmatically
import requests
response = requests.post(
"http://localhost:8080/api/v1/dags/etl_pipeline/dagRuns",
json={"conf": {"date": "2024-06-01"}},
auth=("admin", "admin"),
)
print(response.json()["dag_run_id"])Community Notes
Real experiences from developers who've used this tool