Apache Airflow vs Dagster for ML Pipeline Orchestration
Comparing Airflow and Dagster for orchestrating data and ML pipelines, covering architecture, developer experience, testing, and ML-specific features.
Both Airflow and Dagster orchestrate data and ML pipelines, but they represent different generations of pipeline orchestration philosophy. Airflow is task-centric: define tasks and their dependencies. Dagster is asset-centric: define the data assets your pipeline produces and let Dagster manage the execution. This comparison covers the differences that matter for ML pipeline teams.
Architecture Overview
Apache Airflow (2014) defines workflows as Directed Acyclic Graphs (DAGs) of tasks. Each task is an operator that performs work (run a script, call an API, execute a query). The scheduler triggers tasks based on time schedules and dependency completion. Airflow has a large ecosystem of operators and providers. Apache Airflow 3.0, released April 22, 2025, was the largest release in the project’s history. It added DAG versioning (so a run completes against the version it started with), event-driven and asset-aware scheduling, a new Task Execution API and Python Task SDK that decouple task execution from the metadata database, and a rewritten React UI. The current stable line is Airflow 3.2 (3.2.2 was released May 29, 2026), which narrows the historical feature gap with asset-centric tools by making Airflow itself more asset-aware.
Dagster (2019, built by Dagster Labs) defines workflows around software-defined assets: the data objects that the pipeline produces. Each asset declares what it produces, what it depends on, and how to compute it. Dagster manages materialization (computing assets), scheduling, and lineage automatically. In September 2025, Dagster 1.11.10 marked Components and the new dg CLI as generally available: a framework for packaging reusable, partly YAML-driven pipeline building blocks with ready-made integrations (dbt, Fivetran, Airbyte, Sling, dlt) and scaffolding for new projects.
Feature Comparison
| Feature | Airflow | Dagster |
|---|---|---|
| Core abstraction | Tasks (operators) | Assets (software-defined) |
| DAG definition | Python DAG files | Python with decorators (@asset, @op) |
| Scheduling | Built-in cron scheduler | Built-in schedules and sensors |
| Testing | Limited (mock operators) | First-class (unit test assets locally) |
| Type system | None (XCom is untyped) | Built-in IO managers with types |
| Data lineage | Via plugins (OpenLineage) | Built-in asset graph |
| Partitioning | Time-based partitions | Multi-dimensional partitions |
| Development UI | DAG view, task logs | Asset graph, asset materialization history |
| Configuration | Airflow Variables, Connections | Config system with runtime validation |
| Resource management | Connections, hooks | Resources (dependency injection) |
| Backfills | CLI-based, limited control | UI-based, asset-aware |
| Managed offering | Amazon MWAA, Astronomer, Google Cloud Composer | Dagster+ (Serverless or Hybrid) |
Developer Experience
Pipeline Definition
Airflow pipelines are defined imperatively. You create operator instances and wire them together with >> syntax. This is flexible but can produce complex, hard-to-read DAG files for large pipelines.
Dagster pipelines are defined declaratively. Each @asset function declares its dependencies through function parameters. The dependency graph is inferred from the code, not manually specified. For ML pipelines with clear data dependencies (raw data -> features -> training data -> model -> evaluation), this declarative style maps naturally to the workflow.
Local Development and Testing
Airflow requires a running Airflow instance (scheduler, webserver, database) for development. Testing individual tasks in isolation is possible but requires mocking the Airflow context. Integration testing a full DAG requires the Airflow infrastructure.
Dagster assets are plain Python functions. They can be tested locally with standard pytest, without any Dagster infrastructure. You call the function, pass test inputs, and assert the outputs. This makes TDD practical for pipeline development and significantly reduces the development feedback loop.
Configuration and Parameterization
Airflow uses Variables (global key-value store) and Connections (external system credentials) for configuration. These are stored in the Airflow metadata database and managed through the UI or CLI.
Dagster uses a structured config system with Pydantic-style schemas. Configuration is validated at launch time, not at runtime. This catches configuration errors before the pipeline runs, not 30 minutes into a training job.
ML-Specific Considerations
Experiment Tracking
Neither tool provides built-in experiment tracking, but integration patterns differ. Airflow teams typically integrate MLflow or Weights & Biases through custom operators. Dagster teams integrate through resources (dependency-injected clients), which is cleaner for testing because resources can be swapped with mocks.
Feature Engineering
Dagster’s asset model maps well to feature engineering workflows. Each feature group is an asset with declared dependencies on raw data assets. The asset graph visualizes the full feature lineage, and Dagster can selectively re-materialize specific features when upstream data changes.
Airflow handles feature engineering as task sequences. The lineage is implicit in the DAG structure but is not tied to the data objects themselves.
Model Training and Evaluation
Both tools can orchestrate training jobs. Dagster’s advantage is partition-aware assets: you can define a model asset that is partitioned by training date, and Dagster will manage the relationship between training data partitions and model partitions.
Data Quality
Dagster has built-in asset checks that validate data quality as part of the materialization process. Failed checks can prevent downstream assets from running. Airflow requires external tools (Great Expectations, dbt tests) orchestrated as separate tasks.
When to Choose Airflow
- Existing Airflow investment with established DAGs and team expertise
- Heavy use of Airflow’s operator ecosystem (300+ providers)
- Preference for managed offerings (Amazon MWAA is mature and widely deployed, and added Apache Airflow 3.2 support in May 2026)
- Task-centric workflows where the asset abstraction adds little value
- Organizations where Airflow is the enterprise standard
When to Choose Dagster
- Greenfield ML pipeline projects with no existing orchestrator
- Teams that value local testing and fast development feedback
- Asset-centric workflows where data lineage and selective recomputation matter
- Projects with complex partitioning requirements (multi-dimensional partitions)
- Teams that want built-in data quality checks integrated with orchestration
Migration Considerations
Migrating from Airflow to Dagster is non-trivial but incremental. Dagster provides a migration toolkit called Airlift (the dagster-airlift package) that connects to a live Airflow instance through its REST API and maps existing DAGs to Dagster assets. Airlift follows a staged path (peer, observe, migrate, decommission), letting you move tasks or whole DAGs over one at a time while the rest keep running in Airflow. It works with existing Airflow deployments including Amazon MWAA, Google Cloud Composer, and Astronomer, which reduces migration risk.