Astronomer
Managed Apache Airflow service for orchestrating and monitoring data pipelines in the cloud
Maestro
Scalable workflow orchestrator powering Netflix’s data platform
Maestro delivers a fully managed workflow-as-a-service, handling hundreds of thousands of workflows and millions of jobs daily with strict SLOs, serving data scientists, engineers, analysts, and content teams.
Overview
Overview
Maestro is a general‑purpose workflow orchestrator that delivers a fully managed workflow‑as‑a‑service for data platforms. It powers thousands of internal Netflix users—data scientists, engineers, ML engineers, content producers, and analysts—by scheduling hundreds of thousands of workflows and executing millions of jobs each day while honoring strict service‑level objectives.
Capabilities & Deployment
The platform is built on Java 21 and runs via Gradle, with optional Docker images for AWS or Kubernetes environments. Users interact through a REST API to create, start, monitor, and delete workflows, as demonstrated by the sample curl commands. Maestro’s architecture is highly scalable and extensible, allowing new use cases to be added without disrupting existing pipelines. It maintains performance even during traffic spikes, making it suitable for large‑scale ETL, ML pipelines, and content processing workloads.
Highlights
Fully managed workflow‑as‑a‑service (WAAS)
Handles hundreds of thousands of workflows and millions of jobs daily
Highly scalable and extensible for new use cases
Supports AWS and Kubernetes deployments
Pros
- Proven at Netflix scale with strict SLO compliance
- Supports diverse user roles across data and content teams
- Extensible architecture for evolving workflow requirements
- Robust handling of traffic spikes
Considerations
- Requires Java 21, Gradle, and Docker for operation
- Self‑hosting adds operational complexity
- Tied to the JVM ecosystem
- Documentation may be more internal‑focused
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Fit guide
Great for
- Large data platforms needing reliable workflow orchestration
- Teams requiring strict SLA guarantees
- Organizations building ML/ETL pipelines at scale
- Enterprises integrating with AWS or Kubernetes
Not ideal when
- Small projects with minimal workflow volume
- Teams without Java development expertise
- Environments lacking Docker/Kubernetes support
- Use cases needing lightweight, single‑file scripts
How teams use it
ETL pipeline automation
Streamlines daily data extraction, transformation, and loading across hundreds of jobs
ML model training orchestration
Coordinates data preprocessing, training, and evaluation steps with reproducible runs
Content publishing workflow
Manages end‑to‑end media processing and metadata enrichment
Business analytics reporting
Schedules and runs complex reporting jobs while meeting strict SLA targets
Tech snapshot
Java100%
Shell1%
Frequently asked questions
What runtime environment does Maestro require?
Java 21, Gradle, and Docker are required; it can run on AWS or Kubernetes.
How do I start a workflow?
Use the REST API; sample curl commands are provided in the README.
Is Maestro open source?
Yes, it is released under the Apache‑2.0 license on GitHub.
Can Maestro scale during traffic spikes?
Yes, it maintains strict SLOs even under high load.
Where can I get community support?
Join the community Slack workspace linked in the repository.
Project at a glance
Active- Stars
- 3,706
- Watchers
- 3,706
- Forks
- 255
LicenseApache-2.0
Repo age1 year old
Last commit7 days ago
Primary languageJava
Last synced yesterday