# How to run dlt with Airflow (Or any other Python thing)

DevFeed: [How to run dlt with Airflow (Or any other Python thing)](<https://devfeed.tech/articles/how-to-run-dlt-with-airflow-or-any-other-python-thing-80305.md>)

Original publisher: [Read original article](<https://dlthub.com/blog/dlt-with-airflow>)

Author: Francesco Mucio

Published: 2025-04-02T00:00:00Z

Content type: tutorial

Language: en

Sources: [DLT Hub](<https://devfeed.tech/sources/dlt-hub.md>)

Topics: [CI/CD](<https://devfeed.tech/topics/cicd.md>), [Service fabric](<https://devfeed.tech/topics/service-fabric.md>), [AWS Glue](<https://devfeed.tech/topics/aws-glue.md>)

Tags: [apache-airflow](<https://devfeed.tech/tags/apache-airflow.md>), [aws-fargate](<https://devfeed.tech/tags/aws-fargate.md>), [aws-lambda](<https://devfeed.tech/tags/aws-lambda.md>), [ci-cd](<https://devfeed.tech/tags/ci-cd.md>), [cloud-run](<https://devfeed.tech/tags/cloud-run.md>), [community](<https://devfeed.tech/tags/community.md>), [pipelines](<https://devfeed.tech/tags/pipelines.md>)

## AI overview

The article compares four ways to run dlt pipelines with Apache Airflow: PythonOperator, PythonVirtualenvOperator, KubernetesPodOperator, and external cloud services. It explains tradeoffs involving dependencies, resource use, testing, and infrastructure, and favors KubernetesPodOperator for separating scheduling from execution while acknowledging its operational demands.

## Source excerpt

Explore four ways to run dlt with Apache Airflow, from PythonOperators to KubernetesPods, and learn which setup scales best for clean, reliable pipelines.