Choosing Your Orchestration Tool: Airflow, Dagster, Prefect, and When to Use What

The orchestration tool decision feels paralyzing because you're genuinely stuck with it. Unlike switching databases or adding a new BI tool, changing orchestrators means rewriting every pipeline, retraining your team, and living with two systems during migration.

You're not just picking software - you're choosing your team's daily developer experience for the next 2-3 years. Choose well, and pipelines flow smoothly. Choose poorly, and you're debugging DAGs at 2 AM while your team updates their resumes.

A fond farewell to legacy schedulers

Before we dive into modern orchestration, let's honor the tools that got us here - and acknowledge why it's finally time to let them go.

Cron + bash scripts

You were there when it mattered. You got us to the moon (literally). You ran backups, rotated logs, and scheduled jobs with elegant simplicity. But asking cron to orchestrate complex data pipelines is like asking a bicycle to tow a trailer.

# What started as this...
0 2 * * * /scripts/load_data.sh
# ...became this
0 2 * * * /scripts/load_users.sh && /scripts/load_orders.sh || mail -s "Failed" team@company.com
15 2 * * * /scripts/transform_users.sh
30 2 * * * /scripts/transform_orders.sh
45 2 * * * /scripts/build_metrics.sh || /scripts/retry_metrics.sh
# What if load_users fails but load_orders runs? Good luck figuring it out from cron logs.

Jenkins (for data pipelines)

"It worked for CI/CD, so it'll work for data!" Narrator: It did not work for data. Jenkins thinks in builds and deployments. Data pipelines think in dependencies, backfills, and data quality checks. Close, but fundamentally different.

Control-M

If the six-figure annual license didn't kill your budget, the 1990s-era UI would kill your spirit. Still powering critical infrastructure at Fortune 500 companies where "it works, don't touch it" is the prevailing wisdom.

Autosys

Somewhere, in a basement server room, an Autosys instance is still running. The person who set it up retired in 2009. No one knows how to stop it. No one dares try.

The custom Python scheduler

Started as 500 lines. Grew to 5,000 lines. Now it's 15,000 lines spread across 47 files, and the original author has a "please don't @ me about the scheduler" clause in their LinkedIn headline.

# scheduler.py - Last modified: 2019
# TODO: Refactor this
# TODO: Add tests
# TODO: Fix the retry logic
# FIXME: This breaks on leap years
# NOTE: Don't change this, prod depends on it

The pivot: These tools served their purpose. But modern data pipelines need orchestration - dependency management, backfill handling, monitoring, lineage, testing - not just "run this at 2 AM and hope."

The big three: Airflow, Prefect, Dagster

Apache Airflow: The industry standard

Why teams choose Airflow:

• Massive community: 30,000+ GitHub stars, active Slack, answers for every question
• Connectors for everything: 1,000+ operators and hooks (AWS, GCP, Azure, Snowflake, you name it)
• Mature ecosystem: Monitoring tools, deployment patterns, established best practices
• Managed options: MWAA (AWS), Cloud Composer (GCP), Astronomer all handle ops for you
• Enterprise adoption: When your compliance team asks "is this battle-tested?" - yes

What people complain about:

The UI - Still clunky despite improvements. Finding the right DAG run requires more clicks than it should. Logs are functional but not delightful.

Setup complexity - You can't just pip install airflow and go. You need a database, a scheduler, workers, probably Kubernetes or MWAA. The barrier to "hello world" is higher than it should be.

Development cycle - Write DAG code, wait for scheduler to parse it, refresh UI, hope it works, repeat. The feedback loop is slower than modern developers expect.

XCom limitations - Passing data between tasks uses XCom, which stores everything in the metadata database. Great for small config values, terrible for actual data. You learn to pass S3 paths instead.

Testing is an afterthought - Airflow grew up before testing was standard practice in data engineering. Testing DAGs is possible, but it's not the happy path.

When to use Airflow:

• You already have Airflow (switching costs are real)
• Large team with diverse skill levels (documentation is extensive)
• Need pre-built connectors for legacy systems
• MWAA is already approved in your infrastructure
• Regulatory requirements favor established, audited tools
• Complex multi-team enterprise with governance needs

Example Airflow DAG:

from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.providers.snowflake.operators.snowflake import SnowflakeOperator
from datetime import datetime, timedelta
default_args = {
    'owner': 'data-team',
    'retries': 3,
    'retry_delay': timedelta(minutes=5),
}
with DAG(
    'user_metrics_pipeline',
    default_args=default_args,
    schedule_interval='@daily',
    start_date=datetime(2024, 1, 1),
    catchup=False,
) as dag:
    extract_users = PythonOperator(
        task_id='extract_users',
        python_callable=extract_from_api,
    )
    load_to_warehouse = SnowflakeOperator(
        task_id='load_to_warehouse',
        sql='copy_to_warehouse.sql',
        snowflake_conn_id='snowflake_default',
    )
    transform_metrics = SnowflakeOperator(
        task_id='transform_metrics',
        sql='build_user_metrics.sql',
        snowflake_conn_id='snowflake_default',
    )
    extract_users >> load_to_warehouse >> transform_metrics

Managed options:

• AWS MWAA: Fully managed Airflow on AWS, integrates with IAM, handles scaling
• GCP Cloud Composer: Google's managed Airflow, tight GCP integration
• Astronomer: Multi-cloud managed Airflow with excellent support and tooling

Prefect: Modern Python, better DX

Why teams choose Prefect:

• Pythonic as hell: Write actual Python functions, not DAG configuration. Use decorators, type hints, async/await naturally.
• Local development that works: Run workflows like normal Python scripts. No scheduler, no database, just python my_flow.py.
• Native async support: Modern Python patterns, concurrent execution, proper async/await.
• Excellent observability: The UI is genuinely good. Logs, metrics, lineage - all there, all intuitive.
• Prefect Cloud: The managed offering is top-tier. Good free tier, seamless scaling, excellent DevOps.
• Version 2.0 changed everything: If you tried Prefect 1.x and bounced off, try 2.x. It's a different product.

What people complain about:

Smaller community - Stack Overflow has fewer Prefect answers than Airflow answers. You're more likely to read the source code.

Fewer pre-built integrations - No 1,000+ operator library. You'll write more boilerplate for system connections.

Less enterprise adoption - If your VP asks "who else uses this?" the list is shorter (but growing fast).

When to use Prefect:

• Greenfield project (no legacy to migrate)
• Python-fluent team that values developer experience
• Want faster local development iteration
• Prefect Cloud's pricing model works for you
• Care deeply about code readability and maintainability

Example Prefect flow:

from prefect import flow, task
from prefect_snowflake import SnowflakeConnector
import httpx
@task(retries=3, retry_delay_seconds=300)
async def extract_users():
    async with httpx.AsyncClient() as client:
        response = await client.get("https://api.example.com/users"//api.example.com/users")
        return response.json()
@task
async def load_to_warehouse(data, snowflake_connector):
    await snowflake_connector.execute(
        "COPY INTO users FROM ...",
        parameters={"data": data}
    )
@task
async def transform_metrics(snowflake_connector):
    await snowflake_connector.execute_many([
        "CREATE OR REPLACE TABLE user_metrics AS ...",
        "CREATE OR REPLACE VIEW daily_active_users AS ..."
    ])
@flow(name="user-metrics-pipeline")
async def user_metrics_pipeline():
    connector = SnowflakeConnector.load("snowflake-prod")
    users = await extract_users()
    await load_to_warehouse(users, connector)
    await transform_metrics(connector)
# Run it like a Python script
if __name__ == "__main__":
    user_metrics_pipeline()

Managed options:

• Prefect Cloud: Excellent managed platform with generous free tier, hybrid execution model

Dagster: Software-defined assets

Why teams choose Dagster:

• Asset paradigm is powerful - Once you think in assets (tables, files, models), the framework makes sense
• Testing story is legitimately better - Built-in testing framework, type checking, validation
• Data contracts and type system - Strong typing, schema validation, data quality as first-class
• UI is actually pretty good - Asset lineage visualization, clear dependency graphs, helpful debugging
• Great for dbt workflows - First-class dbt integration, treats dbt models as assets

Balanced reality:

The asset mental model takes adjustment. If you're used to thinking in tasks ("extract, then transform, then load"), switching to assets ("this table depends on these upstream tables") requires rewiring your brain.

More opinionated than alternatives - good if you agree with the opinions (testability, typed assets, declarative dependencies), painful if you don't.

Younger ecosystem means fewer Stack Overflow answers, more source code reading.

What people complain about:

Steeper learning curve - The paradigm shift from tasks to assets isn't trivial. Budget time for team learning.

Opinionated framework - Dagster wants you to do things the Dagster Way. Less flexibility than "just write Python."

Cloud push - Many enterprise features (alerting, SSO, advanced scheduling) push you toward Dagster Cloud.

When to use Dagster:

• dbt-heavy workflows (the integration is chef's kiss)
• Asset lineage and data contracts matter to your use case
• Team values testing and type safety
• Willing to invest in learning curve
• Building data platform with strong governance needs

Example Dagster job:

from dagster import asset, Definitions, SourceAsset
from dagster_snowflake import SnowflakeResource
# Define source data
users_api = SourceAsset(key="users_api")
# Define assets with dependencies
@asset(deps=[users_api])
def raw_users(snowflake: SnowflakeResource) -> None:
    """Extract users from API and load to warehouse."""
    with snowflake.get_connection() as conn:
        conn.execute(
            "COPY INTO raw_users FROM ..."
        )
@asset(deps=[raw_users])
def clean_users(snowflake: SnowflakeResource) -> None:
    """Clean and deduplicate user data."""
    with snowflake.get_connection() as conn:
        conn.execute(
            "CREATE OR REPLACE TABLE clean_users AS "
            "SELECT DISTINCT * FROM raw_users WHERE ..."
        )
@asset(deps=[clean_users])
def user_metrics(snowflake: SnowflakeResource) -> None:
    """Build user metrics from clean data."""
    with snowflake.get_connection() as conn:
        conn.execute(
            "CREATE OR REPLACE TABLE user_metrics AS "
            "SELECT user_id, COUNT(*) as events FROM clean_users GROUP BY 1"
        )
# Define the full set of assets
defs = Definitions(
    assets=[raw_users, clean_users, user_metrics],
    resources={"snowflake": SnowflakeResource(...)}
)

Managed options:

• Dagster Cloud: Managed platform with enterprise features, hybrid execution

Making the decision

Quick decision tree

Already have Airflow?
  → Probably stay unless pain is severe
All-in on dbt?
  → Dagster deserves serious consideration
Want best Python developer experience?
  → Prefect
Need managed AWS solution with minimal config?
  → MWAA (Airflow) or Prefect Cloud
Greenfield project + small team?
  → Prefect or Dagster
Complex multi-team enterprise with governance?
  → Airflow (maturity and ecosystem win)
Team hates writing YAML and loves Python?
  → Prefect or Dagster
Need 100+ pre-built connectors?
  → Airflow

Key decision factors

Red flags for each

Don't choose Airflow if:

• Team actively hates the development experience
• Scale is small (< 10 pipelines) and simplicity matters more
• No one wants to learn Airflow's paradigms

Don't choose Prefect if:

• You need enterprise support that only Airflow's ecosystem provides
• Compliance requires using only well-established tools
• Team is uncomfortable with newer technologies

Don't choose Dagster if:

• Team can't wrap their heads around asset-oriented thinking
• You need traditional task-based orchestration
• Limited bandwidth for learning curve investment

Green flags for each

Choose Airflow when:

• Existing Airflow investment (migration cost > pain cost)
• Need maximum maturity and ecosystem
• Compliance favors established, widely-audited tools
• Team is large and needs extensive documentation
• Pre-built operators save weeks of integration work

Choose Prefect when:

• Developer velocity and experience are top priorities
• Team loves modern Python and async patterns
• Prefect Cloud's model appeals (hybrid execution, generous free tier)
• Greenfield project without legacy constraints

Choose Dagster when:

• dbt is core to your workflow
• Data contracts and lineage are business requirements
• Team values strong typing and testability
• Building data platform with governance from day one

Honorable mentions

Cloud-native orchestration

AWS Step Functions

• Great for: AWS-native teams, serverless workflows, event-driven pipelines
• Skip if: Multi-cloud, need complex data pipeline features
• The sweet spot: Gluing AWS services together (Lambda → S3 → Athena → SNS)

Azure Data Factory

• Great for: All-in Azure shops, visual pipeline builders, enterprise Microsoft stacks
• Skip if: You love writing code (the UI-first approach has limits)
• The reality: Works well for simple pipelines, gets complex fast

GCP Workflows

• Great for: Lightweight orchestration in GCP, YAML-based simplicity
• Skip if: Need complex features, heavy transformation logic
• The niche: Serverless glue for GCP services

Specialized tools

dbt Cloud

• Orchestration specifically for dbt projects
• If dbt is 90%+ of your transformation, this is viable
• Combine with Airflow/Prefect/Dagster for extraction and loading

Temporal

• More for application workflows than data pipelines
• Seeing adoption in data engineering for long-running processes
• Excellent for: Reliability, durability, complex state management

Mage

• Open-source alternative with notebook-based development
• Younger but growing community
• Worth watching if you like Jupyter-style development

Kestra

• Declarative YAML-based orchestration
• Interesting approach to workflow definition
• Early days but promising for teams that prefer config over code

Testing: A key differentiator

One of the biggest gaps in legacy orchestration: testing was hard, so teams didn't do it. Modern tools make testing easier.

Testing in Airflow:

# Testing Airflow DAGs requires setup
import pytest
from airflow.models import DagBag
def test_dag_loads():
    """Verify DAG loads without errors."""
    dagbag = DagBag(include_examples=False)
    assert len(dagbag.import_errors) == 0
def test_task_count():
    """Verify expected number of tasks."""
    dagbag = DagBag()
    dag = dagbag.get_dag('user_metrics_pipeline')
    assert len(dag.tasks) == 3

Testing in Prefect:

# Prefect flows are just Python functions
import pytest
from my_flows import user_metrics_pipeline
def test_user_metrics_pipeline():
    """Test pipeline with mock data."""
    result = user_metrics_pipeline()
    assert result.is_successful()
# Can test individual tasks
async def test_extract_users():
    """Test extraction logic."""
    users = await extract_users()
    assert len(users) > 0
    assert 'user_id' in users[0]

Testing in Dagster:

# Dagster has first-class testing support
from dagster import build_asset_context
from my_assets import clean_users
def test_clean_users():
    """Test asset materialization."""
    context = build_asset_context()
    result = clean_users(context)
    # Dagster validates outputs against type definitions
    assert result is not None

Dagster's testing story is genuinely the best of the three. Prefect's "it's just Python" makes testing natural. Airflow's testing requires more boilerplate.

Series navigation

Related posts:

• Data Platform Observability Best Practices - Monitor whichever tool you choose
• Building Scalable Data Pipelines - Orchestration is just one piece
• Getting Started with Modern Data Stacks - Orchestration in the broader stack

Choosing an orchestration tool for your data platform? We've implemented Airflow, Prefect, and Dagster for teams from startups to Fortune 500s. Let's talk about what fits your specific needs.