Azure offers a visual workflow tool for creating data pipelines as part of its ecosystem. Why should you opt for Dagster instead?
Dagster is a data orchestration platform built specifically for engineers. It lets teams define pipelines and data assets in code, test locally, manage them in Git, and run them with smart scheduling and full lineage visibility.
Azure Data Factory is a GUI-first data integration tool that allows users to build pipelines using a drag-and-drop interface. It’s designed primarily to move and transform data between Azure-native services with minimal coding.
Dagster lets teams define everything in code—from pipeline logic to resources and scheduling. Developers can iterate locally, test pipelines with mocks, and push changes through CI/CD workflows. PRs are readable and reviewable like any other software project.
ADF pipelines are created in a web interface and stored as JSON. This makes version control and testing difficult. Pull requests are hard to review, and there’s no true local development.
Dagster makes data assets first-class. You can track lineage across jobs and teams, set SLAs for freshness, and understand exactly what’s updated, when, and by what. It enables a data mesh approach with true cross-pipeline awareness.
ADF treats datasets as secondary to the pipeline structure. Lineage and status visibility require bolted-on Azure tools, and data asset tracking is limited.
| Feature |  | |
|---|---|---|
| Goal of the solution | Help data engineers define and manage critical data assets. | Cloud ETL service to help ingest data into Azure. |
| Run Python code reliably and provide flexibility for complex programming tasks |
|
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| Data assets |
| Pipeline first — datasets second. |
| Automation |
| Tumbling window schedules, cron schedules with limitations, and some Azure-based event-driven runs. |
| Integration | Asset-first integrations for common data tools | Built around 80+ data ingestion services |
Dagster works well with Azure-hosted environments. Many users deploy Dagster on Azure Kubernetes, use it to orchestrate SQL Server stored procedures, and update PowerBI dashboards. Teams can migrate incrementally from ADF to Dagster without disrupting existing workflows.
Looking for unlimited deployments, advanced RBAC and SAML-based SSO, all on a SOC2 certified platform? Contact the Dagster Labs sales team today to discuss your requirements.
Dagster is a code-first data orchestration platform where pipelines and assets are defined in Python, versioned in Git, and run with strong lineage and testing workflows. Azure Data Factory is a GUI-first Azure service focused on moving data between Azure services with minimal code.
Yes. Teams commonly deploy Dagster on Azure Kubernetes Service and use it to orchestrate workloads that touch SQL Server, Power BI, and other Azure components, often alongside or instead of ADF.
Dagster supports local iteration and automated tests before production runs. ADF is oriented around a web authoring experience with JSON artifacts, which makes local development and familiar software-engineering workflows harder.
Dagster provides asset-first integrations for common data tools in the modern stack. ADF emphasizes a large catalog of Azure-oriented ingestion connectors.
Dagster treats data assets as first-class objects with lineage, partitions, and SLAs. ADF is pipeline-centric, with datasets playing a secondary role relative to pipeline structure.
Dagster supports custom schedules, sensors for event-driven runs, and data SLAs in code, in contrast to ADF's more constrained scheduling and Azure-centric event patterns.
Dagster is open source, and you can self-host or use Dagster+ for managed, enterprise-grade deployment.
Dagster+ is Dagster’s fully managed offering with features like Insights and enterprise controls for teams that want a hosted control plane. You can sign up for a free 30-day trial here.
Dagster University is a great place to learn about Dagster essentials. We have a quickstart guide that walks you through your first pipeline in Python with minimal setup.
