What is Maestro used for?

Maestro is Netflix's open-source workflow orchestrator, a scalable and extensible engine for scheduling data, ML and engineering workflows. It is a good fit for large-scale data pipeline scheduling and machine learning workflow orchestration.

Is Maestro open source?

Yes. Maestro is open source under the Apache-2.0 licence. Its source code is on GitHub at Netflix/maestro and is written mainly in Java.

Yes. Maestro is open source, so the software itself is free to use.

Can I self-host Maestro?

Yes. Maestro can be self-hosted on your own server or infrastructure; there is no official hosted version.

What are some alternatives to Maestro?

Similar open-source tools in the Data Pipelines & ETL category include Airflow, Dagster and Prefect. SaaS products in the same category include Astronomer, AWS Glue and Estuary.

Is Maestro actively maintained?

Yes. The most recent commit to Maestro was on 30 September 2026. The project has 3.8k stars on GitHub.

Maestro: open-source data pipelines & ETL

About Maestro

Maestro is a general-purpose workflow orchestrator from Netflix. Internally it works as a fully managed workflow-as-a-service for data platform users, a community that includes data scientists and engineers, machine learning and software engineers, content producers and business analysts.

According to the README, it schedules hundreds of thousands of workflows and millions of jobs each day and operates under a strict service level objective even during traffic spikes. It is described as highly scalable and extensible, supporting existing and new use cases with an emphasis on usability. Repository topics mention DAGs, ETL and ELT, MLOps, batch processing, data pipelines and agentic workflows. The engine is written in Java.

Maestro is licensed under Apache-2.0 and is run on your own infrastructure; there is no hosted version from the maintainers. It suits data and platform engineering teams that need an orchestrator built for very large-scale scheduling.

Key features

Managed workflow-as-a-service orchestration
Scheduling at very large scale
Extensible for new use cases
DAG-based workflow definitions
Suited to ETL and MLOps pipelines
Written in Java

Good fit for

→Large-scale data pipeline scheduling
→Machine learning workflow orchestration

Built with: Java
Tags: workflow-orchestration; scheduler; data-pipelines; dag; java; mlops; netflix; etl

Maestro: questions and answers

What is Maestro used for?: Maestro is Netflix's open-source workflow orchestrator, a scalable and extensible engine for scheduling data, ML and engineering workflows. It is a good fit for large-scale data pipeline scheduling and machine learning workflow orchestration.
Is Maestro open source?: Yes. Maestro is open source under the Apache-2.0 licence. Its source code is on GitHub at Netflix/maestro and is written mainly in Java.
Is Maestro free?: Yes. Maestro is open source, so the software itself is free to use.
Can I self-host Maestro?: Yes. Maestro can be self-hosted on your own server or infrastructure; there is no official hosted version.
What are some alternatives to Maestro?: Similar open-source tools in the Data Pipelines & ETL category include Airflow, Dagster and Prefect. SaaS products in the same category include Astronomer, AWS Glue and Estuary.
Is Maestro actively maintained?: Yes. The most recent commit to Maestro was on 30 September 2026. The project has 3.8k stars on GitHub.