Udemy

Argo Workflows on Kubernetes - Core Concepts

Name: Argo Workflows on Kubernetes - Core Concepts
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Overview

This is an introductory course to a fuller, hands-on guide to Argo Workflows, the container-native workflow engine for orchestrating jobs on Kubernetes. It covers the core concepts behind Argo: how complex workflows run entirely inside a Kubernetes cluster, how templates and cron workflows let you compose and reuse individual steps, and how Argo integrates with artifact repositories like AWS S3, Minio, and Git.

Who It's For

Kubernetes users and DevOps engineers who want an entry point into Argo Workflows before diving into the full hands-on course, especially anyone working with machine learning, ETL, batch data processing, or CI/CD pipelines.

Key Takeaways

What Argo Workflows is and why it's a Cloud Native Computing Foundation project used by organizations like IBM, SAP, and NVIDIA
How templates and cron workflows enable composable, reusable workflow design
How to interact with Argo using the kubectl CLI and the Argo Server UI
Why Argo can reliably manage thousands of parallel pods and workflows in one cluster

Finishing the course leaves you ready to build your own workflows using Argo's core concepts.

About this course

This is an introductory course to the full course Hands-On Guide to Argo Workflows on Kubernetes.

Argo Workflows is a container native workflow engine for orchestrating jobs in Kubernetes. This means that complex workflows can be created and executed completely in a Kubernetes cluster.

It provides a mature user interface, which makes operation and monitoring very easy and clear. There is native artifact support, whereby it is possible to use completely different artifact repositories (Minio, AWS S3, Artifactory, HDFS, OSS, HTTP, Git, Google Cloud Service, raw).

Templates and cron workflows can be created, with which individual components can be created and combined into complex workflows. This means that composability is given. Furthermore, workflows can be archived and Argo provides a REST API and an Argo CLI tool, which makes communication with the Argo server easy.

It is also worth mentioning that Argo Workflows can be used to manage thousands of parallel pods and workflows within a Kubernetes cluster. And robust repetition mechanisms ensure a high level of reliability.

There is already a large, global community that is growing steadily. Just to name IBM, SAP and NVIDIA. It is mainly used for machine learning, ETL, Batch - and data processing and for CI / CD. And what is also very important - it is open source and a project of the Cloud Native Computing Foundation.

Upon successful completion of the course, you will be able to create workflows using the core concepts of Argo Workflows. You will be confident to use the kubectl CLI and the Argo Server UI in order to communicate with the Argo Server and manage your workflows.