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ARGO WORKFLOWS
Container-native workflow engine for Kubernetes
Apache-2.0
ABOUT
Running complex multi-step jobs on Kubernetes — data processing pipelines, CI/CD workflows, batch jobs — typically requires using different tools for DAG definition, scheduling, artifact management, and monitoring. Argo Workflows unifies all of this as native Kubernetes custom resources. Workflows are defined as YAML DAGs or step templates, each step runs as its own container, artifacts are passed between steps automatically, and the entire execution is visible in the Argo UI. Teams get a Kubernetes-native workflow engine that handles retries, parallel execution, timeouts, and resource limits without additional infrastructure.
INSTALL
brew install argoINTEGRATION GUIDE
1. Orchestrate multi-step data processing pipelines on Kubernetes where each step runs in an isolated container with its own resources
2. Build parallel batch job workflows that fan-out across hundreds of containers and fan-in results for aggregation and reporting
3. Automate ML training workflows with artifact passing between data preparation, training, evaluation, and model registry steps
4. Schedule CI/CD pipelines with conditional branching, retry logic, and timeouts — all defined as Kubernetes YAML manifests
TAGS
workflowkubernetesorchestrationci-cddagcontainersdevops