Introduction
Data orchestration tools are key for managing data pipelines in modern workflows. When it comes to tools, Apache Airflow, Dagster, and Flyte are popular tools serving this need, but they serve different purposes and follow different philosophies. Choosing the right tool for your requirements is essential for scalability and efficiency. In this blog, I will compare Apache Airflow, Dagster, and Flyte, exploring their evolution, features, and unique strengths, while sharing insights from my hands-on experience with these tools in a weather data pipeline project.
Overview
In the weather data project, I got the chance to work with these three tools—Airflow, Dagster, and Flyte, and gained understanding for what makes each one unique. In this blog, I’ll share my experience comparing them and break down how each one works and what sets them apart.
Apache Airflow
Apache Airflow got its start at Airbnb back in October 2014 a Python-based orchestrator with a web interface, designed to handle the company’s growing workflow challenges. It joined the Apache Incubator in 2016 and finally earned its spot as a top-level Apache Software Foundation project in 2019, marking a major milestone in its journey.
Airflow proved to be a blessing, simplifying the management and scheduling of their complex tasks effortlessly.
In the weather data project, I used Airflow to automate the data pipeline, ensuring tasks like fetching, processing, and storing weather data ran in the correct order. Each task depended on the successful completion of the previous one, ensuring smooth and sequential execution from start to finish.
An Airflow DAG file consists of three main components: the DAG instantiation, the tasks, task dependencies and the task order. It looks something like this:
# Dag Instance
@dag(
dag_id="weather_dag",
schedule_interval="0 0 * * *", # Daily at midnight
start_date=datetime.datetime(2025, 1, 19, tzinfo=IST),
catchup=False,
dagrun_timeout=datetime.timedelta(hours=24),
)
# Task Definitions
def weather_dag():
@task()
def create_tables():
create_table()
@task()
def fetch_weather(city: str, date: str):
fetch_and_store_weather(city, date)
@task()
def fetch_daily_weather(city: str):
fetch_day_average(city.title())
@task()
def global_average(city: str):
fetch_global_average(city.title())
# Task Dependencies
create_task = create_tables()
fetch_weather_task = fetch_weather("Alwar", "2025-01-19")
fetch_daily_weather_task = fetch_daily_weather("Alwar")
global_average_task = global_average("Alwar")
# Task Order
create_task >> fetch_weather_task >> fetch_daily_weather_task >> global_average_task
weather_dag_instance = weather_dag()
And it’s all managed through the Airflow UI, which provides a way to monitor and track the progress of the entire pipeline.
DAGSTER
Dagster was developed by Elementl, founded by Nick Schrock, CEO of Elementl in April 2019, With a vision to reshape the data management ecosystem, Nick introduced Dagster—a fresh programming model for data processing.
Unlike traditional tools that focus primarily on tasks or jobs, Dagster emphasizes the relationships between inputs and outputs. Its asset-centric approach focuses on treating data assets as the central units of computation in a pipeline.
Each asset is represented as a dataset and the pipelines revolves around how the assets depend on each other.
@asset(
description='Table Creation for the Weather Data',
metadata={
'description': 'Creates databse tables needed for weather data.',
'created_at': datetime.datetime.now().isoformat()
}
)
def setup_database() -> None:
create_table()
@asset(
deps=[setup_database], # setup_database is a dependency
description="The hourly data",
metadata={
'city and date': f"{city} on {date}",
'created_at': datetime.datetime.now().isoformat()
}
)
def fetch_weather():
weather_data = fetch_and_store_weather(city, date)
return MaterializeResult(
metadata={
'number of rows': weather_data
}
)
@asset(
deps=[fetch_weather], # fetch_weather is a dependency
description="The Day Average",
metadata={
'city and date': f"{city} on {date}",
'created_at': datetime.datetime.now().isoformat()
}
)
def fetch_daily_weather():
weather_data = fetch_day_average(city)
# asset based graphs
columns = ["ID", "City", "Date", "Max Temp (°C)", "Min Temp (°C)", "Condition", "Avg Humidity (%)"]
weather_df = pd.DataFrame(weather_data, columns=columns)
return MaterializeResult(
metadata={
"Row added" : MetadataValue.md(weather_df.head().to_markdown()),
}
)
@asset(
deps=[fetch_daily_weather], # fetch_daily_weather is a dependency
description="The Whole Average",
metadata={
'city': city,
'created_at': datetime.datetime.now().isoformat()
}
)
def global_weather():
fetch_global_average(city.title())
Dagster builds a clear dependency graph, making pipeline transparent and easy to debug.
Traditional Task-Based Workflow
Task 1: Fetch weather data.
Task 2: Clean the data.
Task 3: Store the cleaned data in a database.
Asset-Centric Workflow in Dagster
Asset 1: Raw weather data (produced by fetching from an API).
Asset 2: Cleaned weather data (transformed from raw weather data).
Asset 3: Stored weather dataset (created from cleaned data)
With Dagster, you can build custom asset graphs, linking them directly to pipeline steps. This feature stands out because it’s helping you monitor data as it evolves through each pipeline stage. It adds a level of clarity and interactivity to the workflow, making debugging and monitoring far more intuitive—a functionality I didn’t encounter with Airflow.
And it’s not just asset-centric, if you prefer the task-based approach like in Airflow, Dagster’s got you covered too. You can define your tasks using @ops (operations) in Dagster, just like you’d use @task in Airflow. So whether you're into working with assets or tasks, you’ve got the flexibility to choose the approach that works best for you.
FLYTE
Flyte, a workflow orchestration tool, was initially developed by Lyft in 2016 as an internal platform to manage complex machine learning and data processing pipelines. Later, in 2020 it open-sourced, making it accessible for other companies to use. It leverages Kubernetes, allowing businesses to scale and manage their data and ML workflows in a reliable and efficient way.
Primarily designed to handle both machine learning and data engineering workflows, Built on Kubernetes, Flyte leverages its containerized infrastructure for handling large-scale jobs, which enables efficient resource scaling and management.
In flyte tasks are defined using Python functions and then composed into workflows. Each task represents a unit of work, and tasks can be connected with dependencies, indicating their execution order. It is somewhere similar to airflow task-centric approach.
@task()
def setup_database():
create_table()
@task()
def fetch_weather(city: str, date: str):
fetch_and_store_weather(city, date)
@task()
def fetch_daily_weather(city: str):
fetch_day_average(city)
@task()
def global_weather(city: str):
fetch_global_average(city.title())
@workflow #defining the workflow
def wf(city: str='Noida', date: str='2025-01-17') -> typing.Tuple[str, int]:
# The workflow will execute the tasks in the order they are defined
setup_database()
fetch_weather(city, date)
fetch_daily_weather(city)
global_weather(city)
return f"Workflow executed successfully for {city} on {date}", 0
if __name__ == "__main__":
print(f"Running wf() {wf()}")
Flyte makes local execution easy with flytectl, which sets up a sandbox container for testing workflows. Plus, it lets you run Python code locally, so you can test and debug your workflows before deploying them to the cloud.
Flyte emerges as a modern solution for virtually every aspect of tech workflows, offering the following key benefits
Comparison
Dag Versioning
While working on the weather data project in Airflow, one of the challenges I encountered was managing changes in the pipeline over time—a common issue known as DAG versioning. If you update a pipeline to add or modify tasks, there’s no native way to version these changes, run different versions side by side, or to rollback to previous task. User faces hard-time and more complexity while taking precautions like appending version numbers to DAG IDs, using Git for code tracking, or maintaining separate environments.
In contrast, Dagster solves this problem effectively with its asset-centric approach, built-in support for backfills, and asset snapshots. Each asset is versioned independently, so if any new asset is updated it doesn’t disrupt the other.
As the modern data stack has grown and is still growing, tools can't just limit themselves to only executing, managing, and optimizing data assets anymore. They need to fit into the entire development workflow—from local testing to production deployments—while being cloud-native to support scalability and flexibility.
Whereas,
Flyte addresses DAG versioning seamlessly by supporting versioned workflows natively. When you update or modify a task, Flyte allows you to track and manage different workflow versions without disrupting ongoing processes. Flyte enables you to test updated tasks without affecting the entire workflow, ensuring smoother iteration and flexibility.
Scaling
In data engineering, scaling up is something Dagster handles really well with its flexible architecture for handling large data workflows whereas in Airflow managing resources and scaling can be challenging. However, when it comes to machine learning, Flyte stands out as the more favorable choice, thanks to its built-in support for ML workflows, model versioning, and Kubernetes-based scalability.
Modern AI Orchestration
When you compare Airflow, Dagster, and Flyte, it's clear how they handle different project needs. Like in our weather data project. Airflow excels in scheduling tasks but falls short when you need AI-specific task or handle high-volume, real-time data like in weather prediction models. Dagster focuses heavily on the data-driven approach, which is great, but it lacks some of the dynamic scalability that a complex project like weather forecasting requires. Flyte, however, shines when it comes to AI orchestration. It handles intensive workloads, scales effectively for complex data processing, and automates the workflow, making it ideal for things like predicting weather patterns or managing large sets of weather-related data, all while being efficient and reliable.
CONCLUSION
When deciding between the tools, consider the scale and focus of your workflows. If you need flexibility in pipeline structure and asset management, Dagster is a strong contender. For machine learning workflows with the added benefit of seamless scaling, Flyte should be your go-to solution. Meanwhile, if you are managing straightforward, traditional data engineering tasks, Airflow’s simplicity will still make it a valuable tool. Each of these tools brings unique features and advantages, so understanding your project’s needs will guide you toward the optimal choice.
Top comments (8)
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