Learn ARGO-WORKFLOWS with Real Code Examples

Workflow Controller: schedules workflow steps as Kubernetes pods

Executor: runs individual container steps

Artifact Repository: stores input/output data

Kubernetes API: manages pods and resources

Workflow CustomResourceDefinition (CRD) stores workflow metadata

Workflow CRD defines tasks and dependencies

Controller schedules pods for each step

Pods execute containerized commands

Artifacts passed via object storage

Workflow status and logs available via CLI and UI

Kubernetes CRD-based workflow management

DAG and step templates for modularity

Controller-executor model

Integration with artifact repositories

Event-driven and cron-based execution

ML pipelines orchestrating training, validation, and deployment

ETL pipelines for data lakes

CI/CD pipelines for microservices on Kubernetes

Batch processing and image/video pipelines

Event-driven workflows reacting to external triggers

Kubernetes-native workflow execution

Declarative YAML manifests

Scalable parallel execution

Containerized reproducible steps

Integrates with artifact stores and events

Deploy controller and executor with sufficient resources

Use DAG and parallel steps to scale workloads

Optimize pod resource requests and limits

Distribute workloads across nodes

Use multiple artifact repositories for large workflows

Convert legacy scripts to containerized workflow steps

Define steps and DAG dependencies in YAML

Configure artifact storage and secrets

Submit test workflows to Kubernetes

Integrate with Argo Events or Argo CD if needed