Astronomer
Managed Apache Airflow service for orchestrating and monitoring data pipelines in the cloud
Kestra
Event-Driven Declarative Orchestration Platform for Modern Workflows
Kestra orchestrates scheduled and event-driven workflows using declarative YAML. Build reliable data pipelines, microservices, and automation directly from the UI with Infrastructure as Code best practices.
Overview
What is Kestra?
Kestra is an event-driven orchestration platform designed for teams building reliable, scalable workflows. By combining Infrastructure as Code principles with an intuitive UI, Kestra enables engineers to define both scheduled and real-time workflows in simple YAML—no complex DAG programming required.
Core Capabilities
Define workflows declaratively using a built-in code editor with syntax highlighting, auto-completion, and validation. Execute tasks in any language (Python, Node.js, R, Go, Shell) locally, via Docker, or on Kubernetes. Trigger workflows on schedules or real-time events from Kafka, AWS SQS, Google Pub/Sub, file arrivals, and more. Integrate with cloud platforms (AWS, GCP, Azure), databases, APIs, and big data tools through hundreds of pre-built plugins.
Who Uses Kestra
Data engineers orchestrating ETL pipelines, DevOps teams automating infrastructure tasks, and platform engineers coordinating microservices rely on Kestra. The platform scales to millions of workflows with high availability, fault tolerance, and built-in Git version control. Every UI change automatically updates the YAML definition, ensuring workflows remain code-first while staying accessible to non-developers. Deploy via Docker, Kubernetes, or cloud-native services.
Highlights
Declarative YAML workflows with built-in UI editor, auto-completion, and Git integration
Event-driven triggers for Kafka, SQS, Pub/Sub, file systems, and custom events
Execute scripts in any language (Python, Node.js, R, Go, Shell) on Docker or Kubernetes
Hundreds of plugins for cloud platforms, databases, APIs, and big data processing
Pros
- Everything-as-code approach maintains version control even when building from the UI
- Rich plugin ecosystem supports diverse integrations without custom development
- Scales to millions of workflows with high availability and fault tolerance
- Real-time event triggers enable responsive, event-driven automation
Considerations
- YAML-based configuration may require learning curve for teams unfamiliar with declarative syntax
- Plugin ecosystem breadth may introduce complexity when selecting the right integration
- Docker dependency for local execution requires container runtime setup
- Advanced features like dynamic tasks and conditional branching add configuration overhead
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Fit guide
Great for
- Data engineering teams orchestrating ETL pipelines and batch processing workflows
- DevOps engineers automating infrastructure provisioning and deployment pipelines
- Platform teams coordinating microservices and event-driven architectures
- Organizations requiring Git-based version control and CI/CD integration for workflows
Not ideal when
- Teams seeking low-code solutions without any YAML or configuration file editing
- Projects requiring sub-second latency for real-time stream processing
- Organizations unable to deploy Docker or container-based infrastructure
- Simple cron-job replacements where a basic scheduler suffices
How teams use it
Event-Driven Data Pipelines
Automatically trigger ETL workflows when new files arrive in S3 or messages land in Kafka, processing data in real-time without manual intervention.
Multi-Cloud Infrastructure Automation
Orchestrate provisioning and deployment tasks across AWS, GCP, and Azure using declarative workflows with built-in error handling and retries.
Microservice Coordination
Coordinate complex microservice interactions with conditional branching, parallel execution, and event triggers, maintaining resilience through timeouts and retries.
Scheduled Analytics Workflows
Run daily Python or SQL scripts against BigQuery, Snowflake, or Postgres, generating artifacts and sending Slack notifications on completion or failure.
Tech snapshot
Java71%
Vue19%
TypeScript7%
JavaScript1%
SCSS1%
PLpgSQL1%
Frequently asked questions
How does Kestra maintain workflows as code when building from the UI?
Every change made in the UI automatically updates the underlying YAML definition. You can push workflows to Git directly from Kestra, ensuring version control and CI/CD integration regardless of how workflows are created.
What languages can I use to write scripts in Kestra?
Kestra supports Python, Node.js, R, Go, Shell, and other languages through its plugin ecosystem. Scripts execute locally, in Docker containers, or on Kubernetes depending on your task runner configuration.
Can Kestra handle real-time event-driven workflows?
Yes. Kestra supports real-time triggers for Kafka, AWS SQS, Google Pub/Sub, Azure Event Hubs, file arrivals, and custom events, enabling responsive automation alongside scheduled workflows.
How does Kestra scale for large workflow volumes?
Kestra is designed for high availability and fault tolerance, capable of handling millions of workflows. Deploy on Kubernetes or cloud platforms with horizontal scaling and distributed execution.
Do I need Docker to run Kestra?
Docker is the recommended deployment method for local development and many production scenarios. Kestra also supports Kubernetes, Podman, and cloud-native deployments on AWS, GCP, and Azure.
Project at a glance
Active- Stars
- 26,260
- Watchers
- 26,260
- Forks
- 2,466
LicenseApache-2.0
Repo age6 years old
Last commit3 hours ago
Primary languageJava
Last synced 3 hours ago