Apache Airflow vs Dagster: Side by Side Comparison

Compare Apache Airflow and Dagster for data workflows. Airflow excels in task generation; Dagster offers strong data validation and ML integration.

By

Jatin Solanki

Updated on

October 2, 2024

Apache Airflow and Dagster are open-source platforms used for managing and scheduling data workflows. While they have similar goals, they differ in their approach and features. Apache Airflow is task-based, with dynamic task generation and a web-based user interface, while Dagster is pipeline-based, with strong data validation and error handling and integration with ML frameworks. When choosing between the two platforms, consider your specific needs and use case. Apache Airflow is best for dynamic task generation and integration with tools like Spark and Hadoop, while Dagster is best for strong data validation and error handling or integration with ML frameworks like TensorFlow or PyTorch.

Part 1: Introduction and Overview

Introduce Apache Airflow and Dagster, their features, and their intended use cases.
Explain the importance of comparing the performance of these two platforms.
Provide an overview of what the rest of the article will cover.

‍

Part 2: Comparing Apache Airflow and Dagster

Compare the features and performance of Apache Airflow and Dagster.
Discuss the strengths and weaknesses of each platform.
Provide sample codes for both platforms.

Part 3: Conclusion and Recommendations

Summarize the key points of the article.
Provide recommendations for which platform to choose in different situations.
Conclude in a diplomatic fashion.

Let's get started!

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Part 1: Introduction and Overview

Apache Airflow and Dagster are both open-source platforms designed to manage and schedule data workflows. They allow data engineers to define complex pipelines, track the progress of those pipelines, and manage dependencies between tasks.

Comparing the performance of these two platforms is important because data engineers need to choose the best tool for their specific use case. Understanding the strengths and weaknesses of each platform can help data engineers make informed decisions about which platform to use.

In this article, we will compare the features and performance of Apache Airflow and Dagster. We'll look at sample codes for both platforms and provide recommendations for which platform to choose in different situations.

‍

Part 2: Comparing Apache Airflow and Dagster

Apache Airflow and Dagster have similar goals and features, but they approach those goals in slightly different ways. Here's a breakdown of some of the key features of each platform:

Apache Airflow:

Task-based workflow definition
Dynamic task generation
Built-in operators for common tasks (e.g., PythonOperator, BashOperator, etc.)
Web-based user interface for monitoring and managing workflows
Large community and ecosystem of plugins and integrations

Dagster:

Type-checked, composable pipeline definitions
Automatic tracking of dependencies between tasks
Built-in data validation and error handling
Integration with ML frameworks like TensorFlow and PyTorch
Strong emphasis on testing and reproducibility

Let's take a closer look at some sample code for each platform.

Sample code - Airflow


from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2023, 3, 27),
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=5),
}

dag = DAG(
    'my_dag',
    default_args=default_args,
    description='A simple tutorial DAG',
    schedule_interval=timedelta(days=1),
)

t1 = BashOperator(
    task_id='print_date',
    bash_command='date',
    dag=dag,
)

t2 = BashOperator(
    task_id='sleep',
    bash_command='sleep 5',
    retries=3,
    dag=dag,
)

t2.set_upstream(t1)

This code defines a simple DAG with two tasks: one that prints the current date and time, and another that sleeps for five seconds. The BashOperator is used to run Bash commands, but Airflow has many other built-in operators for different types of tasks.

‍

Sample code for Dagster:


from dagster import pipeline, solid

@solid
def load_data(context):
    return {'data': ...} #
@solid
def preprocess_data(context, data):
    return preprocess(data)

@solid
def train_model(context, preprocessed_data):
    return train(preprocessed_data)

@solid
def evaluate_model(context, trained_model):
    return evaluate(trained_model)

@pipeline
def my_pipeline():
    evaluate_model(train_model(preprocess_data(load_data())))

This code defines a pipeline with four tasks: load_data, preprocess_data, train_model, and evaluate_model. Each task is defined as a solid function, and the pipeline is defined using a @pipeline decorator. Note that the train_model task takes the output of the preprocess_data task as input, and the evaluate_model task takes the output of the train_model task as input. Dagster automatically tracks these dependencies and ensures that tasks are run in the correct order.

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So how do these two platforms compare in terms of performance and features? Here are some things to consider:

Task-based vs. pipeline-based: Apache Airflow is task-based, which means you define each individual task and its dependencies separately. Dagster is pipeline-based, which means you define the entire pipeline as a single unit, with tasks nested inside it. This can make it easier to manage dependencies in complex pipelines.
Dynamic task generation: Apache Airflow allows you to generate tasks dynamically based on data or other factors. Dagster does not have this feature, which can be a limitation in some use cases.
Error handling and validation: Dagster has built-in support for data validation and error handling, which can be very useful in data-intensive workflows. Apache Airflow does not have this feature, although it does have error handling mechanisms for individual tasks.
ML framework integration: Dagster has built-in integration with ML frameworks like TensorFlow and PyTorch. Apache Airflow does not have this feature, although it does have integrations with other tools like Spark and Hadoop.

Overall, both platforms have their strengths and weaknesses. Apache Airflow is a more mature platform with a larger community and ecosystem, while Dagster has some innovative features that make it a good choice for data-intensive workflows.

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Part 3: Conclusion and Recommendations

In conclusion, choosing between Apache Airflow and Dagster depends on your specific use case and needs. If you need a more mature platform with a larger community and ecosystem, Apache Airflow may be the best choice. If you need strong data validation and error handling, or integration with ML frameworks, Dagster may be a better choice.

Here are some specific recommendations:

Choose Apache Airflow if you need to generate tasks dynamically or if you need to integrate with tools like Spark or Hadoop.
Choose Dagster if you need strong data validation and error handling, or if you need integration with ML frameworks like TensorFlow or PyTorch.
Consider both platforms if you need to manage complex workflows with many dependencies and moving parts.

Ultimately, both Apache Airflow and Dagster are powerful tools for managing data workflows, and choosing between them comes down to your specific needs and use case. We hope this article has provided a useful comparison of the features and performance of these two platforms.

Data observability is crucial for maintaining the reliability and accuracy of data workflows, and solutions such as decube can help provide necessary visibility and insights into pipeline performance. Data engineers should explore available solutions like decube to ensure observability in their pipelines. You can signup for decube for free 30 days trial

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