Building a Data Pipeline with Python and Apache Airflow

PythonAirflowData Engineering

Introduction

Data pipelines are the backbone of modern data infrastructure. In this article, we'll explore how to build robust ETL workflows using Apache Airflow and Python.

Why Airflow?

Apache Airflow provides a platform to programmatically author, schedule, and monitor workflows. Its key advantages include:

  • Dynamic pipeline generation — pipelines are defined as code
  • Extensibility — custom operators and hooks
  • Rich UI — for monitoring and debugging

Setting Up the Pipeline

First, let's define our DAG structure:

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime

default_args = {
    'owner': 'data-team',
    'start_date': datetime(2026, 1, 1),
    'retries': 3,
}

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

Data Extraction

The extraction phase connects to various data sources — APIs, databases, and file systems:

def extract_data(**kwargs):
    """Extract data from source systems."""
    import requests
    response = requests.get('https://api.example.com/data')
    return response.json()

Transformation & Loading

Once data is extracted, we apply transformations and load into our data warehouse:

INSERT INTO analytics.daily_metrics
SELECT
    date_trunc('day', event_timestamp) as event_date,
    COUNT(DISTINCT user_id) as unique_users,
    SUM(revenue) as total_revenue
FROM raw.events
WHERE event_timestamp >= CURRENT_DATE - INTERVAL '1 day'
GROUP BY 1;

Conclusion

Building reliable data pipelines requires careful consideration of error handling, monitoring, and scalability. Airflow provides the tools to manage this complexity effectively.

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