Building a Data Pipeline with Airflow

Apache Airflow is a popular open-source platform used to orchestrate, schedule, and monitor data pipelines. It is widely used in data engineering and analytics to automate workflows that move and transform data.

This guide provides a clear overview of how to build a data pipeline using Airflow.

1. What Is a Data Pipeline?

A data pipeline is a series of steps that:

Extract data from a source

Transform or process the data

Load it into a destination (ETL/ELT)

Airflow does not process data itself—it manages and coordinates tasks that do.

2. Why Use Airflow?

Airflow is used because it:

Defines pipelines as code (Python)

Supports scheduling and dependencies

Provides monitoring and retry mechanisms

Scales well for complex workflows

Integrates with many data tools

3. Core Airflow Concepts

DAG (Directed Acyclic Graph)

A DAG represents a workflow

Tasks are nodes; dependencies are edges

DAGs must not contain cycles

Tasks

Individual units of work

Examples: run a SQL query, execute a Python script, load data

Operators

Predefined task templates

Examples: PythonOperator, BashOperator, PostgresOperator

Scheduler and Executor

Scheduler decides when tasks run

Executor runs tasks (Local, Celery, Kubernetes)

4. Typical Data Pipeline Architecture

Example pipeline:

Extract data from an API

Store raw data in cloud storage

Transform data

Load data into a data warehouse

Run data quality checks

5. Setting Up Airflow (High-Level)

Common setup options:

Local machine (for learning)

Docker (recommended)

Cloud-managed services

Key components:

Webserver (UI)

Scheduler

Metadata database

6. Example Airflow DAG (Simple Pipeline)

from airflow import DAG

from airflow.operators.python import PythonOperator

from datetime import datetime

def extract():

print("Extracting data")

def transform():

print("Transforming data")

def load():

print("Loading data")

with DAG(

dag_id="simple_data_pipeline",

start_date=datetime(2024, 1, 1),

schedule_interval="@daily",

catchup=False

) as dag:

extract_task = PythonOperator(

task_id="extract_data",

python_callable=extract

)

transform_task = PythonOperator(

task_id="transform_data",

python_callable=transform

)

load_task = PythonOperator(

task_id="load_data",

python_callable=load

)

extract_task >> transform_task >> load_task

This DAG runs daily and executes tasks in sequence.

7. Managing Dependencies

Airflow ensures:

Tasks run only when upstream tasks succeed

Failed tasks can be retried

Downstream tasks are blocked on failure

This makes pipelines reliable and maintainable.

8. Scheduling and Backfilling

Use cron expressions or presets (@hourly, @daily)

Airflow can backfill missed runs

catchup=False prevents historical runs

9. Monitoring and Logging

Airflow provides:

A web UI for pipeline visualization

Detailed logs for each task

Alerts via email or integrations

This helps quickly identify and fix issues.

10. Best Practices

Keep tasks small and focused

Avoid heavy processing inside Airflow

Use idempotent tasks

Parameterize pipelines

Use connections and variables securely

Version control DAGs

11. Common Use Cases

Data warehouse ingestion

ETL/ELT pipelines

Machine learning workflows

Data quality checks

Reporting automation

12. Limitations of Airflow

Not a streaming tool

Not ideal for real-time processing

Requires operational maintenance

Learning curve for beginners

Conclusion

Apache Airflow is a powerful orchestration tool for building reliable, scalable data pipelines. By defining workflows as code and managing dependencies automatically, Airflow helps teams maintain consistent and transparent data operations.

Learn Data Science Course in Hyderabad

The Modern Data Stack: From Data Lake to Data Warehouse

What is MLOps? A Guide to Bringing Your Models to Production

Move beyond the model to the infrastructure and production side of data science.

Visit Our Quality Thought Training Institute in Hyderabad

Get Directions

December 13, 2025