What is argo?

Argo is a set of open‑source tools that run on Kubernetes. The most common parts are Argo Workflows, which lets you define and run multi‑step jobs (like data pipelines) as code, and Argo CD, which continuously syncs your Git repositories with the state of your Kubernetes clusters for automated deployments.

Let's break it down

  • Kubernetes native: Argo runs as pods inside a Kubernetes cluster, so it uses the same security, scaling, and networking features.
  • Argo Workflows: You write a YAML file that describes a directed‑acyclic graph of steps (tasks). Each step runs in its own container, and the whole workflow can be retried, paused, or visualized.
  • Argo CD: You store the desired state of your apps in Git. Argo CD watches the repo, compares it to what’s actually running, and applies any missing changes automatically.
  • Other pieces: Argo Events (event‑driven automation) and Argo Rollouts (progressive delivery strategies) extend the platform.

Why does it matter?

  • Automation: Turns complex, multi‑container jobs into repeatable code, reducing manual effort.
  • GitOps: Keeps the live environment in sync with version‑controlled definitions, improving auditability and rollback.
  • Scalability: Leverages Kubernetes’ scaling, so workflows can run thousands of parallel tasks.
  • Visibility: Provides UI and CLI tools to monitor progress, logs, and status of each step.

Where is it used?

  • Data processing pipelines: ETL jobs, machine‑learning model training, and batch analytics.
  • CI/CD pipelines: Building, testing, and deploying applications automatically.
  • Infrastructure automation: Running Terraform or Helm commands as part of a workflow.
  • Event‑driven actions: Triggering workflows from webhooks, messages, or sensor data.

Good things about it

  • Open source and free to use.
  • Works natively with any Kubernetes cluster (cloud‑hosted or on‑prem).
  • Declarative YAML definitions make pipelines versionable and reusable.
  • Strong community and many integrations (GitHub, Helm, Prometheus, etc.).
  • UI gives clear visual representation of workflow DAGs and deployment status.

Not-so-good things

  • Requires a Kubernetes cluster; not ideal for teams without K8s expertise.
  • Learning curve for writing correct YAML and understanding Argo’s CRDs.
  • Complex workflows can become hard to debug if logs are not centralized.
  • Resource usage can grow quickly with many concurrent workflows, needing proper quota management.
  • Some features (e.g., advanced rollout strategies) may need additional tools or custom scripting.