Learn ARGO-WORKFLOWS with Real Code Examples

ETL workflow extracting, transforming, and loading data

ML pipeline training and deploying a model

CI/CD pipeline deploying containerized microservices

Batch image processing with parallel steps

Cron workflow generating daily reports

Inspect workflow and pod logs via Argo CLI or kubectl

Check workflow status and step conditions

Verify Kubernetes resources and RBAC permissions

Confirm artifact repository access

Debug DAG dependencies for failures or deadlocks

Submit test workflows with sample containers

Validate YAML manifests using `argo lint`

Check step logs and execution order

Test artifact passing between steps

Monitor resource usage and parallel execution

Deploy workflows via Argo CLI or kubectl

Schedule workflows with cron templates

Trigger workflows via Argo Events

Integrate with GitOps using Argo CD

Use multi-cluster execution for large-scale workloads

Argo CLI for workflow submission and management

Argo UI for visual workflow monitoring

Artifact repositories (S3, GCS, MinIO)

Argo Events for event-driven workflows

Argo CD for GitOps integration

Kubernetes CRDs and native resources

Cloud object storage for artifacts

Git repositories for workflow templates

Notification systems for workflow completion

Machine learning platforms and CI/CD tools

Use reusable templates for common tasks

Organize workflows by namespaces and teams

Cache artifacts to improve performance

Test workflows in dev cluster before production

Integrate with Argo Events for automation

Debugging DAG dependencies and failures

Managing artifacts and storage efficiently

Optimizing workflow resource usage

Monitoring large-scale workflows

Integrating Argo Events and Argo CD pipelines