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Automate ETL with Apache Airflow and MongoDB – A Step-by-Step Guide for Data Engineers

Tired of manual data processing? This tutorial shows you how to build a fully automated ETL pipeline using Apache Airflow and MongoDB —the right way.

You’ll go from zero to a working, production-ready data pipeline that’s easy to monitor, schedule, and scale.

What You’ll Learn
How to install and set up Apache Airflow
How to write DAGs to automate your ETL process
Best practices for connecting Airflow to MongoDB
How to schedule, monitor, and retry tasks in your workflow
Error handling strategies and tips for production deployments

Who This Is For
This tutorial is perfect for:
Data engineers and backend developers
Anyone building data pipelines or learning automation
Teams moving from manual scripts to automated workflows

Tools Used
Python
Apache Airflow
MongoDB
Docker (optional, for local dev)

By the end of this video, you’ll be able to:
Design and run a complete ETL pipeline
Automate data flows from extraction to loading
Confidently manage scheduled jobs using Airflow

Download the source code here: https://github.com/techprane/airflow-...

📌 No prior experience with Airflow required. Just bring your Python basics.

📎 Bonus: All code examples and setup steps are included in the video and linked in the description.