Argo Workflows can orchestrate complex data pipelines, but its true power lies in its ability to manage state and retries across distributed systems, making it more than just a Kubernetes job runner.
Let’s see how this looks in practice with a simple data processing pipeline. Imagine we want to fetch data from an API, process it, and then store it.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: data-pipeline-
spec:
entrypoint: main
templates:
- name: main
dag:
tasks:
- name: fetch-data
template: fetcher
- name: process-data
template: processor
dependencies: [fetch-data]
- name: store-data
template: storer
dependencies: [process-data]
- name: fetcher
container:
image: alpine:latest
command: ["sh", "-c"]
args: ["echo 'Fetching data...' && sleep 5 && echo 'data_payload_from_api' > /data/output.txt"]
volumeMounts:
- name: data-volume
mountPath: /data
- name: processor
container:
image: alpine:latest
command: ["sh", "-c"]
args: ["echo 'Processing data...' && sleep 5 && cat /data/output.txt | sed 's/payload/processed_payload/' > /data/processed.txt"]
volumeMounts:
- name: data-volume
mountPath: /data
- name: storer
container:
image: alpine:latest
command: ["sh", "-c"]
args: ["echo 'Storing data...' && sleep 5 && cat /data/processed.txt && echo 'Data stored successfully.'"]
volumeMounts:
- name: data-volume
mountPath: /data
volumes:
- name: data-volume
emptyDir: {}
This defines a DAG (Directed Acyclic Graph) workflow. The main template orchestrates three tasks: fetch-data, process-data, and store-data. Each task is a separate template (fetcher, processor, storer) that runs a container. Notice how process-data depends on fetch-data, and store-data depends on process-data. This ensures they run in the correct order.
The fetcher task simulates fetching data by writing a string to /data/output.txt. The processor task reads this file, modifies the content, and writes to /data/processed.txt. Finally, the storer task reads the processed data and prints it. The emptyDir volume named data-volume is crucial here: it provides a shared filesystem between pods, allowing data to be passed between steps.
When you apply this YAML (kubectl apply -f your-workflow.yaml), Argo Workflows creates a Workflow custom resource. The Argo controller watches for these resources and schedules pods for each task. You can then monitor the progress using argo get <workflow-name>.
The problem this solves is managing the complexity of multi-step processes that need to be reliable and observable. Instead of writing custom scripts to chain kubectl exec commands or managing complex cron jobs, Argo Workflows provides a declarative way to define, execute, and monitor these pipelines. It handles pod scheduling, dependency management, and provides built-in retry mechanisms.
Internally, each task in the DAG becomes a separate Kubernetes Pod. Argo Workflows tracks the status of each pod. When a pod completes successfully, Argo marks the corresponding task as complete and schedules the next dependent tasks. If a pod fails, Argo can be configured to retry the task, or mark the entire workflow as failed. The emptyDir volume is mounted into each pod, acting as a shared scratch space.
The exact levers you control are within the templates section. You can define container templates for running Docker images, script templates for inline scripts, or even resource templates to interact with Kubernetes objects. Parameters can be passed between steps using {{tasks.task-name.outputs.parameters.param-name}} and artifacts can be passed via persistent volumes or object storage.
What most people don’t realize is how easily you can inject custom logic into Argo Workflows using script templates, which bypasses the need to build custom container images for simple tasks. For instance, you could have a script template that contains Python code to interact with a database or call a cloud API directly, without needing to docker build anything.
The next concept to explore is artifact passing, which allows you to move larger datasets between steps using cloud storage like S3 or GCS, rather than relying solely on ephemeral emptyDir volumes.