What is Argo Workflows?

Argo Workflows is a free, open-source tool that runs on Kubernetes. It lets you describe a series of tasks (a pipeline) and then automatically executes those tasks in the right order, handling any needed resources.

Let's break it down

  • Open-source: Anyone can see the code, use it for free, and help improve it.
  • Tool: A piece of software that helps you do something.
  • Runs on Kubernetes: It works inside a system that manages containers (small, portable pieces of software).
  • Describe a series of tasks: You write down what steps need to happen, like a recipe.
  • Pipeline: The whole set of steps linked together, where the output of one step can become the input of the next.
  • Automatically executes: The tool starts each step at the right time without you having to press a button.
  • Handling any needed resources: It makes sure the right amount of computer power, storage, or other services are available for each step.

Why does it matter?

It saves developers and data teams time and effort by turning manual, error-prone processes into reliable, repeatable workflows. This means faster delivery of software, data analysis, or machine-learning models, and fewer mistakes when moving from development to production.

Where is it used?

  • Data engineering: Running ETL jobs that extract data, transform it, and load it into warehouses.
  • Machine-learning pipelines: Training models, evaluating them, and deploying the best version automatically.
  • CI/CD (continuous integration/continuous deployment): Building, testing, and releasing code updates in a consistent way.
  • Bioinformatics: Orchestrating complex genome-analysis steps that require many specialized tools.

Good things about it

  • Works natively with Kubernetes, so it scales easily with your existing cluster.
  • Declarative YAML definitions make pipelines version-controlled and easy to review.
  • Supports parallel execution, reducing total runtime for large jobs.
  • Rich UI and CLI let you monitor progress and debug failures quickly.
  • Extensible with custom containers, so you can run virtually any tool you need.

Not-so-good things

  • Requires a Kubernetes cluster, which adds operational overhead for teams without it.
  • Learning the YAML syntax and Kubernetes concepts can be steep for beginners.
  • Complex pipelines may become hard to read and maintain if not well-structured.
  • Limited built-in support for non-container workloads; you need to containerize everything.