The big differences between Dagster and Data Factory:
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Azure Data Factory is a drag-and-drop data integration tool that lets you ingest and transform data from different sources.
- Drag-and-drop GUI first, with limited programmatic support
- Used to move data between different Azure services
- Runs manually, on a schedule, or through a limited set of configurable events
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Dagster is specifically designed for data engineers.
- Define critical data assets in code
- Use a declarative approach to make your data engineering team far more productive
- Provides local development, deep integrations with the modern data stack, and scheduling built around stakeholder SLAs.
Software Development Life Cycle and Developer Experience
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Data ingestion, transformation, and control flow is defined in the web interface and saved in JSON.
- Easy to get started, but impossible to customize beyond the pre-defined "building blocks".
- Integrated with source control but requires manual action for testing, PRs are not readable, and no local development.
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The data transformation logic, resource integrations, DAGs, and pipeline automation are all defined and versioned in code.
- Developers can define, review, test, and version every aspect of the data platform locally
- Code PRs are easy to digest and test
- No limits on what transformations, control flows, or source and destination systems can be used, plus support for dynamic programming.
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How ‘data aware’ are these systems?
Azure Data Factory is a pipeline-first system. Datasets are secondary, and dataset lineage requires integrations with other Azure tools.
- Provides limited data lineage.
- New data assets must be wedged into existing pipelines.
- Dependencies across pipelines are not explicit.
With Dagster, data assets are first class citizens.
- Full dataset lineage.
- Clear real-time status of each dataset.
- Pipeline schedules based on data freshness SLAs (i.e. hourly, daily, when upstream dependencies update, etc.).
- Cross-pipeline dependencies are shown to support multi-team data mesh use cases.
To summarize the main differences between Azure Data Factory and Dagster:
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Goal of the solution | Cloud ETL service to help ingest data into Azure. | Help data engineers define and manage critical data assets. |
Run Python code reliably and provide flexibility for complex programming tasks | Azure Data Factory is a code-free platform.
| Python function decorators create DAGs of assets.
|
Data assets | Pipeline first - datasets second. | Asset-centric framework:
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Automation | Tumbling window schedules, cron schedules with limitations, some Azure-based event driven runs. | In Python Code:
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Integrations | Built around 80+ data ingestion services | Asset-first integrations for common data tools |
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Community
Dagster has a growing community of forward-thinking engineers who see the value of our differentiated approach. The Dagster engineering team is directly involved in supporting both open-source and Dagster+ users.
Interested in getting an objective 3rd party perspective? Join the Dagster Slack and interact with current users.