How Can Airflow Be Used in ETL Workflows?

Apache Airflow is a popular ETL automation tool for managing ETL workflows. It provides a declarative, Python-based framework for defining tasks, controlling execution order, and monitoring pipeline health.

 Airflow isn’t an ETL tool by itself, it’s an orchestration layer that lets you define how and when your ETL jobs run, and in what order.

What Is Apache Airflow?

Airflow is an open-source platform developed at Airbnb for:

• Scheduling ETL jobs
• Managing task dependencies
• Monitoring and retrying failed jobs
• Triggering downstream systems

At its core, Airflow lets you define a DAG (Directed Acyclic Graph), where:

• Each node is a task (e.g., extract from MySQL, transform with Python, load into Redshift)
• Edges define dependencies (task B runs after task A)

Why Use Airflow in ETL?

ETL Workflow Example: Airflow DAG

This example shows a 3-step pipeline:

• Extract data from CSV
• Transform using Pandas
• Load into PostgreSQL

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
import pandas as pd
import psycopg2
def extract(): df = pd.read_csv('/data/raw/users.csv') df.to_csv('/data/processed/extracted.csv', index=False)
def transform(): df = pd.read_csv('/data/processed/extracted.csv') df = df[df['status'] == 'active'] df.to_csv('/data/processed/cleaned.csv', index=False)
def load(): df = pd.read_csv('/data/processed/cleaned.csv') conn = psycopg2.connect("dbname=analytics user=etl password=secret") cur = conn.cursor() for _, row in df.iterrows(): cur.execute(""" INSERT INTO users_clean (name, email) VALUES (%s, %s) """, (row['name'], row['email'])) conn.commit() cur.close() conn.close()
with DAG(dag_id='etl_users_pipeline', start_date=datetime(2023, 1, 1), schedule_interval='@daily', catchup=False) as dag: t1 = PythonOperator(task_id='extract', python_callable=extract) t2 = PythonOperator(task_id='transform', python_callable=transform) t3 = PythonOperator(task_id='load', python_callable=load) t1 >> t2 >> t3 # define task dependencies

Best Practices with Airflow in ETL

• Use XComs only for small data (pass file paths instead of full dataframes)
• Separate data and logic — use scripts or modular functions
• Monitor DAG run time trends (to detect anomalies)
• Use Task SLAs to alert on slow or failed jobs
• Store connection credentials securely using Airflow’s Connections UI or Secrets Backend

Built-In Operators Useful in ETL

Final Takeaway

Apache Airflow doesn’t extract or transform data itself. It helps schedule, monitor, and coordinate your ETL tasks. With it, you can turn scattered scripts into reliable, automated workflows that scale. To get the most out of Airflow, it’s worth hiring a Python developer who can build clean, production-ready pipelines.