From Airflow to Prefect: Choosing the Right Orchestration Tool for Your Workflow

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1. Introduction

In my role at a traditional company undergoing digital transformation, I faced a common challenge: managing multiple pipelines across Windows and Linux. These pipelines, essential for generating daily dashboard reports, were difficult to monitor, debug, and scale.

While Airflow is a popular choice for orchestration, it didn’t fit well for our diverse team of engineers and analysts. Prefect, on the other hand, addressed our specific needs with its flexibility and user-friendly features.

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2. The Problem

Our challenges included:

1. Manual Execution: Pipelines on Windows ran manually, while Linux relied on crontab.
2. Monitoring Issues: No centralized way to monitor or trace errors.
3. Complexity for Analysts: Analysts, skilled in SQL but less in Python, found it hard to contribute.
4. Scalability: Adding new workers was cumbersome.

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3. Why Airflow Wasn’t Ideal

• Steep Learning Curve: Difficult for non-engineers to understand and use.
• Windows Support: Running Airflow on Windows required workarounds.
• Scaling Issues: Adding workers or scaling pipelines needed manual effort.

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4. Why Prefect Was the Solution

Prefect solved our problems by offering:

• User-Friendly Syntax: Pythonic workflows easy for both engineers and analysts.
• Cross-Platform Support: Worked seamlessly on both Windows and Linux.
• Real-Time Monitoring: Built-in logging and error handling for quick debugging.
• Easy Scaling: Agents simplified adding more workers as needed.

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5. Steps to Transition

1. Analyze Existing Pipelines: Identify workflows and dependencies.
2. Set Up Prefect: Install and configure Prefect for Windows and Linux.
3. Onboard Analysts: Train analysts to contribute using SQL-friendly workflows.
4. Monitor and Debug: Use Prefect’s UI to track pipeline execution.
5. Scale When Needed: Deploy agents for additional capacity.

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6. Prefect vs. Airflow

Feature	Airflow	Prefect
Ease of Use	Complex for non-engineers	Intuitive and Pythonic
Windows Support	Limited	Excellent
Monitoring	Manual log tracing	Built-in tools
Scalability	Requires expertise	Simple agent setup

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7. Airflow Code vs. Prefect Code: A Comparison

Below is a side-by-side comparison of the same workflow implemented in Airflow and Prefect.

• Airflow code:

  from datetime import datetime, timedelta
  
  from airflow import DAG
  from airflow.operators.bash import BashOperator
  from airflow.operators.python import PythonOperator
  
  
  def task1():
      print("Running Task 1")
  
  def task2():
      print("Running Task 2")
  
  # Default arguments for the DAG
  default_args = {
      'owner': 'airflow',
      'depends_on_past': False,
      'email': ['[email protected]'],
      'email_on_failure': False,
      'email_on_retry': False,
      'retries': 1,
      'retry_delay': timedelta(minutes=5),
  }
  
  # Define the DAG
  dag = DAG(
      'd_02_example_dag_dependency',
      default_args=default_args,
      description='An example DAG with Python operators',
      schedule="* * * * *",
      start_date=datetime(2023, 1, 1),
      catchup=False
  )
  
  # Define the tasks
  task1_obj = PythonOperator(
      task_id='task1',
      python_callable=task1,
      dag=dag,
  )
  
  task2_obj = PythonOperator(
      task_id='task2',
      python_callable=task2,
      dag=dag,
  )
  
  task3_obj = BashOperator(
      task_id='task3',
      bash_command='echo "Hello from Task 3!"',
      dag=dag,
  )
  
  # Task dependencies
  task1_obj >> task2_obj
  task1_obj >> task3_obj
• Prefect code:

  from prefect import flow, task
  from prefect.tasks.shell import ShellOperation
  
  
  @task
  def task1():
      print("Running Task 1")
  
  
  @task
  def task2():
      print("Running Task 2")
  
  
  @flow(name="d_02_example_dag_dependency")
  def example_flow():
      # Define the tasks
      t1 = task1()
      t2 = task2()
      t3 = ShellOperation(commands=["echo 'Hello from Task 3!'"]).run()
  
      # Set task dependencies
      t2.wait_for(t1)
      t3.wait_for(t1)
  
  
  if __name__ == "__main__":
      example_flow()

Key Differences

Aspect	Airflow	Prefect
Setup	Requires defining a DAG explicitly.	Uses Python functions and decorators.
Task Definition	Separate task objects (Python/Bash).	Python @task decorator for simplicity.
Dependency Management	Explicit with >> and << operators.	Handled with .wait_for() or function calls.
Execution	Deployed to Airflow Scheduler.	Runs as a Python script or with Prefect Cloud.
Cross-Platform Support	Limited for Windows.	Cross-platform (Windows/Linux).

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8. Conclusion

Prefect’s simplicity, flexibility, and user-friendly design make it ideal for diverse teams and hybrid environments. By transitioning to Prefect, we unified our workflows, empowered analysts, and improved scalability—proving that the right tool can transform the way we work.

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