Kestra

Kestra

by [Kestra (kestra.io)]

An open-source, event-driven workflow orchestration platform that combines declarative YAML flows, a visual topology editor, and a plugin ecosystem for data pipelines and automation.

See https://kestra.io

Features

• Declarative YAML-based flow definitions (flows, tasks, namespaces) with templating and runtime substitution.
• Language-agnostic task execution (run scripts in any language locally or in containers).
• Rich plugin ecosystem (hundreds of community and official plugins for AWS, databases, HTTP, cloud services, etc.).
• Event-driven triggers: schedules, webhooks, API, event sources with low-latency execution.
• UI with topology (DAG) visualizer, drag-and-drop editor, embedded code editor with autocompletion, and run/backfill controls.
• Built-in retry policies, timeouts, conditional branching, concurrency controls, and worker groups.
• Observability: centralized logs, execution history, metrics, and revision history for flows.
• CI/CD and Git-friendly: flows as code, Terraform provider, and Git-sync workflows.
• Extensible plugin architecture for custom task types and integrations.

Superpowers

Kestra is designed for teams who want “orchestration as code” with first-class support for heterogeneous tooling and languages. Its core strengths:

• Declarative + visual: Teams can author flows in YAML (versioned in Git) while using the UI to inspect and operate them.
• Language and runtime freedom: reuse existing scripts and binaries without rewriting into a proprietary DSL.
• Event-driven scale: supports real-time and scheduled workflows with mature retry and error-handling primitives.
• Extensible integrations: broad plugin catalog lets teams connect to cloud services and data stores without custom glue code.

Who should use it: platform engineers, data engineering teams, and automation-focused SRE/DevOps teams that need reliable, scalable orchestration across mixed runtimes and services.

Pricing

• Open-source core: free to run on your infrastructure under the project license.
• Commercial / hosted offerings: Kestra offers a cloud/hosted product and enterprise features (check kestra.io for current tiers and licensing details).

Practical usage notes

• Common patterns: ETL/data pipelines (extract → transform → load), serverless API orchestrations (invoke Lambda, query DB, fan-out), infra tasks (provisioning, config rollout), and event-driven business processes.
• Execution model: tasks can run locally on worker nodes or be containerized; Kestra manages I/O between tasks and captures logs automatically.
• Workflow lifecycle: flows are defined in YAML, stored in Git, validated in the UI/editor, and executed via API, schedule, or event trigger. Backfills and replays are supported from the UI.

Small YAML example (illustrative)

This minimal example demonstrates the shape of a Kestra flow. It is simplified — consult official docs for full schema details.

id: example::hello_world  
namespace: default  
tasks:  
  - id: echo  
    type: io.kestra.core.tasks.debugs.Echo  
    format: "Hello, Kestra!"  

Comparison

• Apache Airflow: Airflow is Python-centric (DAGs as Python code) and excels at scheduling and batch pipelines. Kestra uses declarative YAML, is language-agnostic, and emphasizes an event-driven model plus a visual topology editor.
• Argo Workflows: Argo is Kubernetes-native and ideal for containerized, K8s-first workloads. Kestra is platform-agnostic (can run outside K8s) and focuses on broader orchestration patterns (scripts, APIs, plugins) with richer built-in task types.
• Apache NiFi: NiFi targets dataflow and streaming with a flow-based visual programming model focused on data routing and transformation. Kestra provides orchestration primitives, retries, and scheduling with a code-friendly YAML approach while offering visual topology views.

Examples / Real-world applications

• Data engineering pipelines that stitch together many data sources, transforms, and loads with retries and backfills.
• Serverless orchestrations that coordinate multi-step Lambda/DynamoDB/HTTP workflows.
• Operational automation for infra tasks, periodic maintenance, and complex multi-system rollouts.

Where to learn more

• Official site: https://kestra.io
• GitHub: https://github.com/kestra-io/kestra
• Docs and examples: see the Kestra documentation site and GitHub examples for plugins, flow patterns, and operator guidance.