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For machine learning teams, the more useful question is not which orchestrator is better in the abstract. It is how each tool represents the artifacts you must produce, refresh, validate and trace: datasets, feature tables, trained models, evaluation reports and deployed model versions. Airflow now supports asset-aware scheduling, so a downstream DAG can run when an upstream asset is updated. Dagster is built around software-defined assets, where each asset is tied to its upstream dependencies and to the code that produces it. Those are two different starting points, and the right choice depends on which one matches your pipeline.
Why “Airflow or Dagster?” is the wrong first question
A tool comparison starts from features: operators, UI, scheduler, plugins. An ML team’s pain usually starts from a different place. A model is trained on a snapshot of data, evaluated against a reference set, and promoted to serving. When something goes wrong, the team needs to answer three questions quickly: which data produced this model, which code ran, and what has to be rebuilt when an input changes.
Orchestrators answer those questions differently. Some treat the unit of work as a task in a graph and let you attach data events to it. Others treat the output as the primary object and derive the work from it. The distinction is one of emphasis and representation, not a verdict that one tool wins across the board. The sections below compare the two models on the axes that matter for ML work.
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Airflow’s documentation describes assets as logical groupings of data, identified by URIs. Airflow makes no assumptions about the content or physical location of the data behind a URI. The identifier is a name the team agrees on, and Airflow uses it to link producers and consumers. The URI does not, by itself, tell Airflow how the data was built or where it lives.
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Asset-aware scheduling is the feature that makes this useful. Airflow can schedule a downstream DAG based on updates to upstream assets, and the documentation states this capability was added in Airflow 2.4. Time-based schedules still exist alongside it, so a team can run a pipeline on a cron-style cadence, on asset updates, or on a mix of both.
What this looks like in practice
Imagine a feature-engineering DAG that writes a training table. If that DAG declares the table as an asset, a training DAG can be configured to start when the table is updated rather than at a fixed hour. The training DAG does not need to know how the feature table was built. It only needs the asset identifier to be consistent between the two DAGs.
How Dagster represents assets
Dagster’s central abstraction is the software-defined asset. Each asset has an asset key, a list of upstream asset keys, and the computation that produces it. The dependency graph is therefore part of the code that defines the asset, and the lineage between a model and the data it was built from is expressed directly rather than inferred from task ordering.
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Dagster’s documentation lists persisted ML models among the things that can be modeled as assets. That fits ML work well: a trained model file can be declared as an output with a clear upstream chain from the training dataset and the code that trained it.
Side-by-side comparison
The table compares the two models on the dimensions that affect ML pipelines. Where the sources reviewed for this article do not settle a point, the cell says so.
| Dimension | Airflow | Dagster |
|---|---|---|
| Primary modeling unit | DAG tasks, with assets as data events that tasks can emit or consume | Software-defined assets, each with upstream asset keys and a producing computation |
| Asset identity | Logical group identified by a URI; no assumption about content or location | Asset key defined in code, linked to the computation that produces it |
| Trigger behavior | Time-based schedules and asset-aware scheduling (added in Airflow 2.4, per Airflow’s documentation) | Not compared on this axis in the sources reviewed for this article |
| Dependency representation | Expressed through DAG structure and asset updates | Expressed through upstream asset keys in the asset definition |
| Model artifacts | Assets can represent model outputs; the lifecycle distinctions are described in AIP-74 | Persisted ML models are listed in Dagster’s documentation as a possible asset type |
| Operational fit (deployment, integrations, team experience) | Not settled by the sources reviewed; evaluate locally | Not settled by the sources reviewed; evaluate locally |
The ML artifact lifecycle
An ML platform has to make the lifecycle of each asset explicit. A task can replace an asset, append to it, or publish a new iteration of it, such as a new model version. These are different operations with different consequences for lineage and rollback. Replacing a feature table overwrites the previous state; appending keeps history but changes what downstream steps read; publishing a new model version preserves earlier models so that a deployment can be rolled back.
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Airflow’s AIP-74 describes these distinctions. When you compare orchestrators, check whether your team can express each of these three operations as a clear, named action, and whether the tool records which version of an asset each downstream run consumed. Those are the questions that decide whether debugging a bad model takes minutes or days.
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A practical way to choose
Start with one representative ML workflow rather than a product list.
- Draw the artifacts. Typically: source data, a feature or training dataset, a trained model, evaluation results, and the deployment artifact.
- Mark the durable assets. For each artifact, ask whether another team, a later run, or an audit needs to address it by name.
- Mark the dependencies that must be visible. Decide which upstream links need to be explicit in code and in the UI, not implied by task order.
- Name the triggering event for each downstream step. Is it a schedule, an upstream data update, or a new model version? Write it down per step.
- Match the lifecycle. For each asset, decide whether it is replaced, appended to, or published as a new version, and confirm the tool can express that.
The answer that comes out of this exercise tends to fall into one of three branches:
- If the center of gravity is a code-defined asset graph with clear model and data lineage, Dagster’s software-defined asset model maps directly onto that picture.
- If the team already runs Airflow DAGs and mainly needs downstream DAGs to respond to declared data updates, Airflow’s asset-aware scheduling covers that need within the existing environment.
- If the workflow mixes both needs, a team should check how each tool handles cross-asset dependencies before standardizing on either one.
Local questions the sources do not settle
Several factors matter a great deal in practice and are not resolved by the official material reviewed here. Evaluate them with your own environment in mind:
- Migration effort from existing DAGs or jobs.
- Provider and integration coverage for your data stores, compute and model registry.
- Deployment model: self-hosted, managed, or a combination.
- Where compute runs, and how each tool hands work to it.
- Your team’s existing familiarity with either tool.
Learning resources
Data Pipelines with Apache Airflow, Second Edition is the most current print reference for Airflow readers. Manning lists it as a 512-page book published in January 2026, covering Airflow 3 and including ML examples; the Simon & Schuster listing identifies a trade paperback edition. For Dagster, Dagster University offers official hands-on courses, including Dagster Essentials and Dagster & dbt. Check current availability and pricing on the publishers’ and Dagster’s own pages before buying.
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Airflow and Dagster both change quickly. Verify the current feature set in each project’s documentation before committing to an architecture, particularly for asset-scheduling behavior and provider support.
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