Argo Workflows

About Argo Workflows

Open-source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Designed for large-scale computational tasks with powerful workflow features.

Key Features

• 1Kubernetes-native workflow engine defining pipelines as YAML-based DAGs
• 2Container-based task execution with each step running in its own Docker container
• 3Artifact passing between pipeline steps via S3, GCS, or local storage
• 4Parallel and fan-out/fan-in execution patterns for scalable batch processing
• 5Argo Events integration for event-driven pipeline triggering

How Python Data Engineers Use Argo Workflows

Python data engineers use Argo Workflows on Kubernetes to orchestrate containerised data pipelines where each task runs as an isolated Docker container. Python scripts are packaged as container images and executed as workflow steps, enabling reproducible, scalable batch processing. Argo is particularly popular in ML engineering teams for model training pipelines and in cloud-native data platforms where Kubernetes is the deployment target.