--- title: "dagster vs maestro" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dagster-io-dagster-vs-netflix-maestro" tools: ["dagster-io-dagster", "netflix-maestro"] --- # dagster vs maestro *GraphCanon updated Sep 20, 2026* ## Verdict Pick dagster if dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows; pick maestro if maestro is Netflix's workflow orchestrator built to manage complex workflows and data pipelines using advanced scheduling and automation features. [dagster](https://dagster.io) reports 16k GitHub stars, 2.3k forks, and 2.6k open issues, last pushed Sep 11, 2026. [maestro](https://maestro-doc.github.io) has 3.8k stars, 311 forks, and 59 open issues, last pushed Sep 16, 2026. Figures are from public GitHub metadata via [dagster's repository](https://github.com/dagster-io/dagster) and [maestro's repository](https://github.com/Netflix/maestro). | | [dagster](/tools/dagster-io-dagster.md) | [maestro](/tools/netflix-maestro.md) | | --- | --- | --- | | Tagline | An orchestration platform for data assets | Netflix's Workflow Orchestrator | | Stars | 16,144 | 3,836 | | Forks | 2,290 | 311 | | Open issues | 2,587 | 59 | | Language | Python | Java | | Adopt for | Dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows. | Maestro is Netflix's workflow orchestrator built to manage complex workflows and data pipelines using advanced scheduling and automation features. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Maestro is licensed under the Apache-2.0 license, allowing wide usage but with an 'AS IS' basis and no warranties or conditions stated. | | Categories | Data & Retrieval, Evaluation & Observability | Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dagster](/tools/dagster-io-dagster.md) | [maestro](/tools/netflix-maestro.md) | | --- | --- | --- | | Days since push | 2d | 0d | | Open issues (now) | 2.6k | 59 | | Stars delta | +195 (30d) | +24 (30d) | | Open issues delta | -9 (30d) | +25 (30d) | | Full report | [trust report](/tools/dagster-io-dagster/trust.md) | [trust report](/tools/netflix-maestro/trust.md) | ## Shared compatibility - **Python**: [dagster](/tools/dagster-io-dagster.md) - Python runtime; [maestro](/tools/netflix-maestro.md) - Python runtime ## Decision facts: dagster - **Adopt for:** Dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows. ## Decision facts: maestro - **Requirements:** To install Maestro, ensure you have pip available to run `pip install maestro-sdk`, which is required for initiating use. - **Adopt for:** Maestro is Netflix's workflow orchestrator built to manage complex workflows and data pipelines using advanced scheduling and automation features. - **License detail:** Maestro is licensed under the Apache-2.0 license, allowing wide usage but with an 'AS IS' basis and no warranties or conditions stated. ## Choose when ### Choose dagster if… - dagster is primarily Python; maestro is Java. - Tags unique to dagster: data-orchestrator, etl, mlops, workflow. - Also covers Data & Retrieval, Evaluation & Observability. - When your project requires an Apache-2.0 licensed tool allowing broader reuse and modification of code. ### Choose maestro if… - maestro is primarily Java; dagster is Python. - Requirements: To install Maestro, ensure you have pip available to run `pip install maestro-sdk`, which is required for initiating use.. - Tags unique to maestro: agentic-workflow, analytics, automation, batch-processing. - Also covers Developer Tools. - When your team requires support for complex workflows specifically enhanced by Netflix's engineering expertise, Maestro offers a tailored solution. ## When NOT to use dagster - If you are restricted to proprietary or non-open-source licenses, as Dagster's Apache-2.0 might not align with compliance requirements. - In environments where Python is not a preferred language, considering Dagster requires good knowledge of the Python ecosystem. - For teams that do not require or benefit from extensive documentation and hands-on tutorials for onboarding. - If specific features or integrations crucial to your workflow are found lacking in comparison to competitors. ## When NOT to use maestro - Avoid using Maestro if your project requires lightweight solutions or integrates tightly with tools from other big tech firms with conflicting ecosystem priorities. - Do not opt for Maestro if you need a tool without significant dependencies on Java, as it might complicate setups for teams working in a less Java-centric environment. ## Common questions ### What is the difference between dagster and maestro? dagster: An orchestration platform for data assets. maestro: Netflix's Workflow Orchestrator. See the comparison table for live GitHub stats and shared categories. ### When should I choose dagster over maestro? Choose dagster over maestro when dagster is primarily Python; maestro is Java; Tags unique to dagster: data-orchestrator, etl, mlops, workflow; Also covers Data & Retrieval, Evaluation & Observability; When your project requires an Apache-2.0 licensed tool allowing broader reuse and modification of code. ### When should I choose maestro over dagster? Choose maestro over dagster when maestro is primarily Java; dagster is Python; Requirements: To install Maestro, ensure you have pip available to run `pip install maestro-sdk`, which is required for initiating use.; Tags unique to maestro: agentic-workflow, analytics, automation, batch-processing; Also covers Developer Tools; When your team requires support for complex workflows specifically enhanced by Netflix's engineering expertise, Maestro offers a tailored solution. ### When should I avoid dagster? If you are restricted to proprietary or non-open-source licenses, as Dagster's Apache-2.0 might not align with compliance requirements. In environments where Python is not a preferred language, considering Dagster requires good knowledge of the Python ecosystem. For teams that do not require or benefit from extensive documentation and hands-on tutorials for onboarding. If specific features or integrations crucial to your workflow are found lacking in comparison to competitors. ### When should I avoid maestro? Avoid using Maestro if your project requires lightweight solutions or integrates tightly with tools from other big tech firms with conflicting ecosystem priorities. Do not opt for Maestro if you need a tool without significant dependencies on Java, as it might complicate setups for teams working in a less Java-centric environment. ### Is dagster or maestro more popular on GitHub? dagster has more GitHub stars (16,144 vs 3,836). Stars measure visibility, not whether either tool fits your constraints. ### Are dagster and maestro open source? Yes - both are open-source projects on GitHub (dagster: Apache-2.0, maestro: Apache-2.0). ### Where can I find alternatives to dagster or maestro? GraphCanon lists graph-backed alternatives at [dagster alternatives](/tools/dagster-io-dagster/alternatives) and [maestro alternatives](/tools/netflix-maestro/alternatives) ([dagster markdown twin](/tools/dagster-io-dagster/alternatives.md), [maestro markdown twin](/tools/netflix-maestro/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/dagster-io-dagster-vs-netflix-maestro.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dagster or maestro? dagster: Very active. maestro: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for dagster and maestro? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dagster trust report](/tools/dagster-io-dagster/trust); [maestro trust report](/tools/netflix-maestro/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dagster-io-dagster`](/api/graphcanon/graph?tool=dagster-io-dagster) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_