--- title: "dagster vs great_expectations" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dagster-io-dagster-vs-fivetran-great-expectations" tools: ["dagster-io-dagster", "fivetran-great-expectations"] --- # dagster vs great_expectations *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 great_expectations if great Expectations (GX) is a Python-based tool for data quality testing and documentation, offering expressive unit tests and automated documentation to maintain high standards of data integrity. [dagster](https://dagster.io) reports 16k GitHub stars, 2.3k forks, and 2.6k open issues, last pushed Sep 11, 2026. [great_expectations](https://docs.greatexpectations.io/) has 12k stars, 1.9k forks, and 47 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [dagster's repository](https://github.com/dagster-io/dagster) and [great_expectations's repository](https://github.com/fivetran/great_expectations). | | [dagster](/tools/dagster-io-dagster.md) | [great_expectations](/tools/fivetran-great-expectations.md) | | --- | --- | --- | | Tagline | An orchestration platform for data assets | Always know what to expect from your data. | | Stars | 16,144 | 11,798 | | Forks | 2,290 | 1,850 | | Open issues | 2,587 | 47 | | Language | Python | Python | | Adopt for | Dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows. | Great Expectations (GX) is a Python-based tool for data quality testing and documentation, offering expressive unit tests and automated documentation to maintain high standards of data integrity. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Data & Retrieval, Evaluation & Observability | Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dagster](/tools/dagster-io-dagster.md) | [great_expectations](/tools/fivetran-great-expectations.md) | | --- | --- | --- | | Days since push | 2d | 0d | | Open issues (now) | 2.6k | 47 | | Stars delta | +195 (30d) | +108 (30d) | | Open issues delta | -9 (30d) | +8 (30d) | | Full report | [trust report](/tools/dagster-io-dagster/trust.md) | [trust report](/tools/fivetran-great-expectations/trust.md) | ## Shared compatibility - **Python**: [dagster](/tools/dagster-io-dagster.md) - Python runtime; [great_expectations](/tools/fivetran-great-expectations.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: great_expectations - **Adopt for:** Great Expectations (GX) is a Python-based tool for data quality testing and documentation, offering expressive unit tests and automated documentation to maintain high standards of data integrity. ## Choose when ### Choose dagster if… - Tags unique to dagster: data-orchestrator, etl, mlops, workflow. - Also covers Evaluation & Observability. - When your project requires an Apache-2.0 licensed tool allowing broader reuse and modification of code. ### Choose great_expectations if… - Tags unique to great_expectations: cleandata, data-profilers, data-profiling, data-quality. - When you need a tool that provides expressive and extensible unit tests for your data, fostering collaboration through a common language for data quality tests. - More recently updated (last pushed Sep 18, 2026). ## 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 great_expectations - If your project is not in Python or if you are working with Python versions earlier than 3.10 or later than 3.13 without enabling experimental support. - When your team prefers a more manual approach to data quality testing and documentation, as GX Core emphasizes automated and collaborative processes. - If your data quality needs are simple and do not require the extensive documentation and testing capabilities provided by GX Core. - For projects that require real-time data quality monitoring and alerting, as GX Core focuses more on batch processing and documentation rather than real-time monitoring. ## Common questions ### What is the difference between dagster and great_expectations? dagster: An orchestration platform for data assets. great_expectations: Always know what to expect from your data.. See the comparison table for live GitHub stats and shared categories. ### When should I choose dagster over great_expectations? Choose dagster over great_expectations when Tags unique to dagster: data-orchestrator, etl, mlops, workflow; Also covers Evaluation & Observability; When your project requires an Apache-2.0 licensed tool allowing broader reuse and modification of code. ### When should I choose great_expectations over dagster? Choose great_expectations over dagster when Tags unique to great_expectations: cleandata, data-profilers, data-profiling, data-quality; When you need a tool that provides expressive and extensible unit tests for your data, fostering collaboration through a common language for data quality tests; More recently updated (last pushed Sep 18, 2026). ### 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 great_expectations? If your project is not in Python or if you are working with Python versions earlier than 3.10 or later than 3.13 without enabling experimental support. When your team prefers a more manual approach to data quality testing and documentation, as GX Core emphasizes automated and collaborative processes. If your data quality needs are simple and do not require the extensive documentation and testing capabilities provided by GX Core. For projects that require real-time data quality monitoring and alerting, as GX Core focuses more on batch processing and documentation rather than real-time monitoring. ### Is dagster or great_expectations more popular on GitHub? dagster has more GitHub stars (16,144 vs 11,798). Stars measure visibility, not whether either tool fits your constraints. ### Are dagster and great_expectations open source? Yes - both are open-source projects on GitHub (dagster: Apache-2.0, great_expectations: Apache-2.0). ### Where can I find alternatives to dagster or great_expectations? GraphCanon lists graph-backed alternatives at [dagster alternatives](/tools/dagster-io-dagster/alternatives) and [great_expectations alternatives](/tools/fivetran-great-expectations/alternatives) ([dagster markdown twin](/tools/dagster-io-dagster/alternatives.md), [great_expectations markdown twin](/tools/fivetran-great-expectations/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-fivetran-great-expectations.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dagster or great_expectations? dagster: Very active. great_expectations: 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 great_expectations? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dagster trust report](/tools/dagster-io-dagster/trust); [great_expectations trust report](/tools/fivetran-great-expectations/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/_