Comparing Prefect and Dagster (and why not Airflow)

The Orchestration Challenge

As data pipelines grow in complexity, the need for reliable workflow orchestration becomes critical. Orchestration tools help manage the execution of tasks, handle dependencies, schedule runs, and provide observability into your data workflows.

For years, Apache Airflow has been the dominant solution in this space. However, newer tools like Prefect and Dagster have emerged, offering modern approaches that address many of Airflow’s limitations, particularly for lean, edge data stacks.

In this article, we’ll compare Prefect and Dagster, examining their strengths and unique features. We’ll also discuss why you might choose these newer alternatives over Airflow, especially in lightweight data environments.

Why Not Airflow?

Before diving into Prefect and Dagster, let’s address the elephant in the room: Apache Airflow. While Airflow is battle-tested and widely adopted, it has several limitations that make it less suitable for modern edge data stacks:

Airflow Limitations

• Heavy Infrastructure Requirements: Airflow requires significant infrastructure to run properly, including a metadata database, scheduler, and workers. This overhead can be excessive for smaller projects.

• Static DAG Definition: Airflow DAGs are defined statically, making dynamic workflow creation challenging. The DAG is defined separately from its execution, creating a disconnect between definition and runtime.

• Python 2.7 Legacy: Although newer versions support Python 3, Airflow was built with Python 2.7, and many design decisions reflect this legacy.

• Limited Parametrization: Passing data between tasks in Airflow can be cumbersome, often requiring XComs, which weren’t designed for large data transfers.

• Complex Setup and Maintenance: Setting up and maintaining Airflow can be complex, with multiple services to configure and monitor.

• Testing Difficulties: Testing Airflow DAGs locally can be challenging, requiring mock environments or complex setups.

For these reasons, modern alternatives like Prefect and Dagster have gained popularity, particularly for teams embracing the edge data philosophy.

Prefect: Workflows as Code

Prefect reimagines workflow orchestration as “workflows as code,” emphasizing developer experience and ease of use.

Key Features of Prefect

• Pythonic Workflows: Define workflows directly in Python with minimal boilerplate, making pipelines more intuitive and easier to maintain.

• Dynamic Flows: Create workflows that adapt to inputs and conditions at runtime, unlike Airflow’s static DAGs.

• Local Development: Easy local development and testing with minimal setup required.

• Hybrid Execution Model: Run workflows locally or in distributed environments seamlessly.

• Functional API: Build workflows using functional programming concepts, with tasks as functions that can be composed and reused.

• Rich Observability: Comprehensive monitoring, alerting, and visualization of workflow executions.

• Deployment Options: Deploy anywhere from a laptop to Kubernetes, with flexible scaling options.

Prefect Code Example

Here’s a simple Prefect workflow example:

from prefect import flow, task

@task
def extract():
    # Extract data from source
    return [1, 2, 3, 4, 5]

@task
def transform(data):
    # Transform the data
    return [x * 10 for x in data]

@task
def load(data):
    # Load the data to destination
    print(f"Loading data: {data}")
    return "Success!"

@flow
def etl_flow():
    data = extract()
    transformed = transform(data)
    result = load(transformed)
    return result

if __name__ == "__main__":
    etl_flow()

Dagster: A Data-Aware Orchestrator

Dagster approaches orchestration with a stronger focus on data assets and software engineering principles.

Key Features of Dagster

• Data-Centric Approach: Dagster treats data as a first-class concept, with explicit typing of inputs and outputs.

• Asset-Based Workflows: Define workflows in terms of data assets and their interdependencies, not just tasks.

• Software Engineering Best Practices: Strong emphasis on testability, modularity, and separation of concerns.

• Rich Type System: Define schemas for data passing between tasks, enabling validation and documentation.

• Environment Configuration: Separate business logic from environment-specific configuration, making deployments more flexible.

• Incremental Computation: Efficiently recompute only what’s needed based on changes to inputs.

• UI for Data Assets: Visualize and interact with data assets, not just task executions.

Dagster Code Example

Here’s a simple Dagster workflow:

from dagster import asset, materialize

@asset
def raw_data():
    # Extract data from source
    return [1, 2, 3, 4, 5]

@asset
def transformed_data(raw_data):
    # Transform the data
    return [x * 10 for x in raw_data]

@asset
def final_result(transformed_data):
    # Load the data to destination
    print(f"Loading data: {transformed_data}")
    return "Success!"

if __name__ == "__main__":
    # Materialize all assets
    result = materialize([raw_data, transformed_data, final_result])

Comparing Prefect and Dagster

Now let’s directly compare these two modern orchestrators across several dimensions:

Design Philosophy

• Prefect: Emphasizes “workflows as code” with a focus on developer experience and flexibility. Prefect aims to make workflow creation as Pythonic and intuitive as possible.

• Dagster: Focuses on “data-aware” orchestration with strong typing, testability, and separation of business logic from execution configuration.

Learning Curve

• Prefect: Generally easier to get started with, especially for Python developers. The API feels natural and minimizes boilerplate.

• Dagster: Slightly steeper learning curve due to its more structured approach and stronger typing system, but offers more guardrails once learned.

Data Flow

• Prefect: Uses Python objects to pass data between tasks, with automatic serialization/deserialization when needed.

• Dagster: Explicitly types inputs and outputs, with a focus on data assets rather than just task execution.

Local Development

• Prefect: Extremely easy local development with minimal setup. Run flows directly as Python scripts.

• Dagster: Good local development experience with the Dagster UI available locally, though requires slightly more setup than Prefect.

UI/Observability

• Prefect: Clean, modern UI focused on flow runs and task states.

• Dagster: Comprehensive UI that visualizes both asset dependencies and task executions, with more emphasis on data lineage.

Community and Adoption

• Prefect: Rapidly growing community with strong momentum, particularly in data science and analytics use cases.

• Dagster: Growing community with strong adoption in data engineering teams that value software engineering principles.

Fit for Edge Data Stacks

Both Prefect and Dagster work well in edge data stacks, but with different strengths:

• Prefect: Excels when you need lightweight, flexible workflows that are quick to develop and easy to maintain.

• Dagster: Shines when data assets and their relationships are central to your workflows, or when you need stronger guardrails around data passing between tasks.

Which One Should You Choose?

The choice between Prefect and Dagster often comes down to your team’s preferences and specific requirements:

Choose Prefect if:

• Developer experience and minimal boilerplate are priorities
• You need highly dynamic workflows that adapt at runtime
• You prefer a functional programming approach
• Your team values simplicity and quick iteration
• Your workflows involve complex control flow with branching and conditionals

Choose Dagster if:

• Data assets and their relationships are central to your workflows
• You value strong typing and explicit schemas
• Software engineering principles like testability are important
• Your organization benefits from clear separation of business logic and configuration
• You need to track data lineage and asset dependencies

Getting Started

Both tools are remarkably easy to get started with, especially compared to Airflow:

Prefect Quick Start

# Install Prefect
pip install prefect

# Start a local Prefect server (optional for development)
prefect server start

# Create and run a flow
python your_prefect_flow.py

Dagster Quick Start

# Install Dagster
pip install dagster dagster-webserver

# Start the Dagster UI
dagster dev

# Create and execute assets
python your_dagster_assets.py

Integrating with Your Edge Data Stack

Both Prefect and Dagster integrate nicely with other components of an edge data stack:

• With DuckDB: Both can orchestrate DuckDB queries and transformations, either directly or through dbt.

• With dbt: Both offer dbt integrations to orchestrate dbt model runs and track dependencies.

• With visualization tools: Outputs from either tool can feed directly into Evidence.dev, Marimo, or other visualization tools.

Conclusion

Prefect and Dagster represent the modern approach to workflow orchestration, addressing many of Airflow’s limitations while bringing fresh perspectives to data pipeline management. For edge data stacks, these lighter-weight, more developer-friendly tools offer significant advantages over traditional heavyweight orchestrators.

While Airflow remains a solid choice for large enterprises with existing investments in the ecosystem, teams building new data platforms—especially those embracing the edge data philosophy—should seriously consider Prefect or Dagster for their orchestration needs.

Your choice between them should be guided by your team’s preferences, your specific requirements, and whether you value Prefect’s flexibility and simplicity or Dagster’s structured, data-centric approach more highly.