I was standing up a distributor ETL for Unilever, watching SFTP files land at odd hours and trigger dbt runs downstream, when the orchestrator question got real. The three tools on the table didn't just differ on syntax. They couldn't even agree on what a pipeline is. Airflow saw a sequence of tasks. Dagster saw a graph of data assets. Prefect saw a bunch of Python functions with some scheduling bolted on. Pick the wrong mental model and you fight your tool for a year.
So this is less a feature bake-off than a way to choose. I've run these in anger, including a build that pushed 50M+ records a day, and the deciding factor was never the marketing. It was whether the tool's worldview matched how the data actually moved.
The one decision that separates these tools
Everything else is detail. The fork in the road is what each orchestrator treats as the primary unit of work. That one choice shapes the developer experience, the testing story, the cost, and how each tool fails on you at 3am.
In plain terms:
- Airflow orchestrates tasks. You describe a DAG of operations ("run this, then that") and it guarantees order, scheduling and retries.
- Dagster orchestrates assets. You declare the data objects you want to exist (a table, a model, a file) and how each is derived. It computes the graph and tracks lineage for you.
- Prefect orchestrates functions. You decorate normal Python with
@flowand@task, and it adds scheduling, retries and observability with almost no structural ceremony.
Hold onto that distinction. The rest of this comparison falls out of it.
Apache Airflow: the incumbent standard
My short verdict: nobody gets fired for picking Airflow, and on a managed service it's the one I reach for when the pipeline is plainly a sequence of tasks.
Airflow has been the default since 2014, and Airflow 3.x modernised it considerably: a faster React UI, DAG versioning, and data-aware scheduling via assets. Ask any platform team "what do you use for orchestration?" and Airflow is still the statistical answer. That ubiquity is its biggest advantage.
Where Airflow wins
- Ecosystem and integrations. Hundreds of provider packages and operators for nearly every database, cloud and SaaS. Need to talk to some obscure system? Someone already wrote the operator.
- Managed offerings. AWS MWAA, Google Cloud Composer and Astronomer all run Airflow for you, so you're not on the hook for the scheduler, metadata DB and workers.
- Hiring. The talent pool knows Airflow, so onboarding rarely stalls on the orchestrator.
Where Airflow hurts
- Local development is the heaviest of the three. You spin up a scheduler, webserver, metadata database and executor just to test one DAG.
- It thinks in tasks, not data. Lineage and "is my table fresh?" are bolted on rather than native. The newer asset features help, but it isn't Dagster's home turf.
- Dynamic pipelines. Generating tasks at runtime is possible, but historically awkward.
from airflow.decorators import dag, task
@dag(schedule="@daily", catchup=False)
def sales_etl():
@task
def extract(): ...
@task
def transform(rows): ...
@task
def load(rows): ...
load(transform(extract())) # Airflow wires the task order from this graph
sales_etl()Dagster: the asset-first challenger
My short verdict: this is the one I picked for the Unilever build, and the asset model earned back its learning curve within the first month of lineage actually being visible.
Dagster reframes the problem. Instead of "run these tasks in this order," you declare software-defined assets, the tables, models and files you want to exist, and Dagster works out the execution graph, tracks lineage, and shows the freshness of every asset in a catalog.
Where Dagster wins
- Asset lineage out of the box. You see your data graph: what's stale, what failed, what depends on what. For analytics and ML platforms this is enormous.
- Best-in-class local dev and testing. Assets are plain Python you can unit-test without spinning up infrastructure. Typed inputs and outputs catch errors before they ship.
- First-class dbt integration. Dagster loads your dbt models as assets, so SQL transforms and Python steps live in one lineage graph.
Where Dagster hurts
- The mental model. "Think in assets, not tasks" is a genuine shift. Teams that just want to run a script on a cron can find it over-structured at first.
- A smaller ecosystem than Airflow's provider zoo, though the core connectors are solid and growing fast.
from dagster import asset
@asset
def raw_sales() -> list[dict]: ...
@asset
def clean_sales(raw_sales: list[dict]) -> list[dict]: ...
@asset
def sales_mart(clean_sales: list[dict]) -> None:
# Dagster derives the graph + lineage from the dependencies above
...Prefect: the Pythonic lightweight
My short verdict: when I just need a working script on a schedule by end of day, Prefect gets out of the way faster than anything else here.
Prefect (now on 3.x) optimises for developer happiness. You write normal Python functions, decorate them, and you have a scheduled, observable, retrying workflow. The control flow is dynamic and runtime-defined, and it feels native because it is just Python.
Where Prefect wins
- Lowest friction. The jump from "a script that works" to "a scheduled, monitored flow" is the smallest of the three.
- Dynamic workflows. Branching, mapping and runtime-generated tasks are natural. That helps when a pipeline's shape depends on the data.
- Hybrid execution. Run flows on your own infrastructure while Prefect Cloud handles orchestration and observability, keeping your data in your environment.
Where Prefect hurts
- Lineage and cataloguing are limited. It's flow-centric, not asset-centric, and a data catalog with per-table freshness is Dagster's wheelhouse.
- Fewer guardrails. Its flexibility means larger teams sometimes want more structure than Prefect imposes.
from prefect import flow, task
@task(retries=3)
def extract(): ...
@task
def transform(rows): ...
@flow(log_prints=True)
def sales_etl():
load(transform(extract())) # plain Python — Prefect adds the orchestrationSide-by-side comparison
| Dimension | Airflow | Dagster | Prefect |
|---|---|---|---|
| Core abstraction | Tasks (DAGs) | Software-defined assets | Decorated Python flows |
| Data lineage | Add-on (assets/datasets) | Native, first-class | Limited |
| Local dev / testing | Heaviest | Lightest, typed | Light |
| Dynamic pipelines | Awkward | Good | Excellent |
| Ecosystem / integrations | Largest | Growing | Moderate |
| dbt integration | Good | Best (as assets) | Good |
| Managed cloud | MWAA, Composer, Astronomer | Dagster+ | Prefect Cloud |
| Learning curve | Medium | Medium–high | Low |
| Best fit | Big, task-shaped, managed | Data / analytics platforms | Pythonic, dynamic flows |
What about cost?
The license cost is zero. All three are open source. The cost that actually bites is the infrastructure and the engineering time to run them.
- Airflow self-hosted has the highest operational surface (scheduler, metadata DB, workers); managed Airflow removes that for a monthly fee.
- Dagster and Prefect are lighter to self-host, and both offer hybrid cloud tiers where you pay for orchestration while keeping compute in your own account.
So which should you actually use?
Map it back to the data, not the feature list. Dagster if your ETL is a graph of data assets, you use dbt, or lineage and testing matter. Airflow if you need the widest ecosystem, managed hosting, or you're joining a team that already runs it. Prefect if your team is Python-first and you want the shortest path from script to production.
A decision rule, top to bottom:
- Default to Airflow if you want the most-supported, easiest-to-hire-for option and you'll run it managed. Rarely the most elegant choice. Rarely the wrong one.
- Choose Dagster if your work is fundamentally about data assets and lineage, such as analytics engineering, dbt-heavy stacks, or ML feature pipelines. The asset model pays back its learning curve fast.
- Choose Prefect if you want the shortest path from Python to production and your pipelines are dynamic or small-to-mid scale.
There's no universally "best" tool here, only the best fit for your team's shape and your data model. Get the data model right and any of the three will serve you well.
Frequently asked questions
Is Dagster better than Airflow?
For asset-centric, dbt-heavy data platforms where lineage and testing matter, Dagster is usually the more productive choice. For a broad, task-shaped workload that needs the largest ecosystem and managed hosting, Airflow is still hard to beat. "Better" depends on whether you think in tasks or assets.
Is Prefect easier than Airflow?
Yes. For most teams Prefect has a noticeably lower learning curve and lighter local setup, because flows are just decorated Python functions and dynamic control flow is native.
Can I migrate from Airflow to Dagster or Prefect?
Yes, and it's common. The hard part is rarely the API. It's re-modelling your pipeline (tasks → assets, or tasks → flows) and moving scheduling, secrets and connections. Migrate one pipeline first to learn the patterns before committing the whole platform.
Which is best for dbt?
Dagster, because it loads dbt models as native assets in a single lineage graph. Airflow and Prefect both run dbt well via integrations, but they don't unify SQL and Python lineage the way Dagster does.
How long does it take to migrate from Airflow to Dagster?
For a single pipeline, budget 2–4 days once you've learned the asset model. Most of the work is redesigning your task graph as assets, not the Python itself. For a full platform migration, allow 4–8 weeks for a parallel-run phase where you validate that Dagster outputs match the existing Airflow runs before cutting over.
Conclusion
None of these tools is objectively best. The decision is really about matching the orchestrator's worldview to your pipeline. Airflow orchestrates tasks. Dagster orchestrates data assets. Prefect orchestrates Python functions. Work out which of those your ETL actually is, and the choice mostly makes itself. That's the same lesson the Unilever build kept teaching me: the model fit the data first, and the tool just followed.
Once you've chosen your orchestrator, the next question is usually where the data lands. The companion guide on Snowflake vs Redshift vs BigQuery migration covers that decision, and if AI workloads are part of your stack, the AI cost optimisation guide shows how to keep inference spend from ballooning as the pipelines scale.
If you're standing up or untangling a data pipeline and want it done right the first time, that's the work I do. See how I scope and price the work, explore my data engineering case studies, or get in touch and let's scope it.
Mirza Hammad Tariq
AWS Data Engineer with 5+ years building production-grade ETL pipelines, cloud data warehouses, and scalable data architectures in Python, SQL, Dagster, and AWS.