Data Orchestration Showdown: Airflow vs. Dagster

4 min readDec 3, 2024

Data orchestration is the backbone of modern data engineering, ensuring workflows run smoothly, dependencies are managed, and errors are handled gracefully. Among the myriad of tools, Apache Airflow and Dagster stand out as two powerful contenders. Both tools automate workflows, but their design philosophies and capabilities differ significantly. In this article, we’ll compare these tools with examples, real-world use cases, and insights from companies to help you make an informed decision.

What Is Data Orchestration?

Data orchestration is the process of automating, scheduling, and managing data workflows. A typical workflow might include tasks like:

Extracting Data: Pulling data from APIs, databases, or files.
Loading Data: Storing data in a data warehouse or database for analysis.
Transforming Data: Cleaning, aggregating, or modeling data.

An orchestration tool ensures these tasks run in the right order, handle failures gracefully, and provide monitoring and logging for debugging.

The Contenders

Apache Airflow

Developed by Airbnb in 2014, Apache Airflow is a powerful, Python-based orchestration tool. It uses Directed Acyclic Graphs (DAGs) to define workflows as Python code. Airflow is widely adopted across industries and is known for its flexibility and ecosystem.

Key Stats (as of December 2024):

• GitHub Stars: ~32.7k
• Forks: ~13.6k
• Contributors: ~2.5k

Notable Users:

Airbnb, Uber, Slack, and Lyft rely on Airflow for orchestrating complex workflows.

Features:

• Extensive Ecosystem: Includes prebuilt operators for major cloud providers and services (e.g., AWS, Google Cloud).
• Dynamic Workflows: Python-powered workflows allow dynamic task generation.
• Mature Scheduler: Handles large-scale, distributed workflows.

Dagster

Introduced in 2019 by Elementl, Dagster is a modern orchestration tool that emphasizes data awareness, type safety, and developer experience. Dagster reimagines workflows as modular, testable units of computation called ops and graphs.

Key Stats (as of December 2024):

• GitHub Stars: ~6.9k
• Forks: ~750
• Contributors: ~250

Notable Users:
Companies like Wayfair, Convoy, and Elementl use Dagster for data orchestration.

Features:

• Data-Aware: Tracks inputs, outputs, and metadata natively.
• Type Safety: Reduces runtime errors by enforcing strong typing.
• Integrated Testing: Simplifies testing and debugging workflows.
• Modern UI: Provides rich visualizations for monitoring workflows.

Hands-On Comparison

To better illustrate the differences, let’s implement a simple data pipeline that:

1. Extracts sales data from an API.

2. Loads the data into PostgreSQL.

3. Generates a summary report.

Apache Airflow Example

Here’s how you might define this pipeline in Airflow:

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime
def extract_data():
  print("Extracting data from API…")
  return {"sales": 1000, "revenue": 50000}
def load_to_postgres(data):
  print(f"Loading data to PostgreSQL: {data}")
def generate_report():
  print("Generating sales report…")
with DAG('sales_pipeline', start_date=datetime(2024, 1, 1), schedule_interval='@daily') as dag:
  extract = PythonOperator(
    task_id='extract',
    python_callable=extract_data )
  load = PythonOperator(
    task_id='load',
    python_callable=lambda: load_to_postgres(extract.output))
  report = PythonOperator(
    task_id='report',
    python_callable=generate_report)
  
extract >> load >> report

Dagster Example

Here’s the same pipeline in Dagster:

from dagster import job, op, Out

@op(out=Out(dict))
def extract_data():
  print("Extracting data from API…")
  return {"sales": 1000, "revenue": 50000}

@op
def load_to_postgres(data):
  print(f"Loading data to PostgreSQL: {data}")

@op
def generate_report():
  print("Generating sales report…")

@job
def sales_pipeline():
  data = extract_data()
  load_to_postgres(data)
  generate_report()

Dagster Advantages:

• Type Safety: The @op decorator enforces that data passed between tasks has the correct type.

• Data Visibility: Dagster’s UI shows data passing through the pipeline, making debugging easier.

Real-World Insights

Apache Airflow in Action

• Lyft uses Airflow to orchestrate ETL pipelines that process massive ride-sharing data. Airflow’s scalability and robust ecosystem were critical for managing thousands of tasks daily.

• Slack employs Airflow to manage workflows for data analytics and customer insights, leveraging its integration with cloud services like AWS and Snowflake.

Dagster in Action

• Wayfair adopted Dagster for its machine learning workflows, valuing the tool’s modularity and ability to track data lineage.

• Convoy, a logistics company, uses Dagster to manage data pipelines for optimizing freight operations. Dagster’s type safety reduced runtime errors, improving reliability.

Feature-by-Feature Comparison

-------------
| Feature           | Apache Airflow                        |Dagster
| Ease of Use       | Steep learning curve, script-heavy    | Developer-friendly, modular design||
| Type Safety       | No native support                     | Strongly enforced
| Data Awareness    | Limited                               | Native tracking of datasets
| Ecosystem         | Extensive integrations and operators  | Growing but smaller
| Flexibility       | Fully dynamic workflows in Python     | Composable, reusable components
| Monitoring        | Logging-based                         | Rich metadata and visualizations
| Community Support | Large, active, and established        | Rapidly growing, smaller community

When to Use Each Tool

Choose Apache Airflow If:

• You need extensive integrations with third-party tools (e.g., AWS, GCP).

• Your team is experienced with Python and can manage dynamic workflows.

• Scalability and community support are priorities.

Example:

A global enterprise like Uber uses Airflow for orchestrating ETL jobs across distributed systems, where the tool’s robust scheduling capabilities and large community are invaluable.

Choose Dagster If:

• You want data-aware orchestration with native type safety.

• Testing, debugging, and observability are critical.

• You’re building modern data pipelines or machine learning workflows.

Example:

A startup developing machine learning models might adopt Dagster for its ability to track data lineage and enforce correctness in pipelines.

Final Verdict

Both Airflow and Dagster are excellent tools, but they cater to different needs:

• Apache Airflow is the industry standard for large-scale data workflows, with a robust ecosystem and a mature community.

• Dagster is a modern alternative focusing on developer experience, data observability, and type safety, ideal for teams building modular, testable pipelines.

Summary

• For traditional ETL workflows: Go with Airflow.

• For modern, composable pipelines: Choose Dagster.

What has your experience been with Airflow or Dagster?

References:

• Apache Airflow GitHub

• Dagster GitHub

• Airbnb Engineering Blog on Airflow

• Dagster Success Stories