Data Engineering: Building ETL Pipelines with Apache Airflow or Dagster

Introduction

ETL (Extract, Transform, Load) pipelines are fundamental in data engineering, enabling efficient data movement and transformation. These pipelines help process vast amounts of data from multiple sources and prepare it for analysis. Two powerful tools for orchestrating ETL workflows are Apache Airflow and Dagster.

Apache Airflow

What is Apache Airflow?

Apache Airflow is an open-source workflow automation tool used to schedule, manage, and monitor ETL processes. It allows users to define tasks as Directed Acyclic Graphs (DAGs), ensuring dependencies are met before executing subsequent tasks.

Key Features of Apache Airflow

• Scalability with distributed execution
• Integration with various data sources
• Robust monitoring and logging
• Flexible scheduling options
• Python-based DAG creation

Installing Apache Airflow

pip install apache-airflow

Setting Up Apache Airflow

Once installed, initialize the Airflow database and start the webserver:

airflow db init
airflow webserver --port 8080

Creating an ETL Pipeline in Airflow

A simple Airflow DAG consists of tasks that perform ETL operations. Below is an example of an ETL pipeline using PythonOperators.

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

def extract():
    print("Extracting data from source")

def transform():
    print("Transforming data")

def load():
    print("Loading data into destination")

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2023, 1, 1),
    'retries': 1,
}

dag = DAG('etl_pipeline', default_args=default_args, schedule_interval='@daily')

extract_task = PythonOperator(task_id='extract', python_callable=extract, dag=dag)
transform_task = PythonOperator(task_id='transform', python_callable=transform, dag=dag)
load_task = PythonOperator(task_id='load', python_callable=load, dag=dag)

extract_task >> transform_task >> load_task

Dagster

What is Dagster?

Dagster is an orchestration framework for data pipelines that focuses on data quality, observability, and testing.

Key Features of Dagster

• Data-driven workflow design
• Built-in testing and validation
• Parallel task execution
• UI for debugging and monitoring

Installing Dagster

pip install dagster dagit

Setting Up Dagster

Initialize a new Dagster project:

dagster project scaffold -n my_etl_project

Creating an ETL Pipeline in Dagster

Dagster uses ops (tasks) and jobs (workflows). Below is an example of an ETL job.

from dagster import job, op

@op
def extract():
    return "Extracted Data"

@op
def transform(data):
    return data.upper()

@op
def load(data):
    print(f"Loading: {data}")

@job
def etl_pipeline():
    load(transform(extract()))

Running the Dagster Pipeline

dagit -f etl_pipeline.py

Comparison: Airflow vs. Dagster

Feature	Apache Airflow	Dagster
Workflow Style	DAG-based	Data-driven
Ease of Use	More complex setup	Simpler with built-in testing
Monitoring	UI and logs	More detailed insights

Conclusion

Both Apache Airflow and Dagster are powerful ETL orchestration tools. Airflow is better suited for large-scale workflow automation, while Dagster offers a data-centric approach with built-in validation.