Data orchestration tools play a crucial role in modern data engineering workflows. Two popular options in this space are Apache Airflow and Dagster. While both aim to help data teams build and manage complex data pipelines, they take different approaches and are better suited for different use cases. This article will compare Dagster and Airflow to help you understand their key differences and choose the right tool for your needs.

Overview

Airflow

Airflow is a highly popular, open-source workflow management system known for its flexibility, ease of use, and strong community support. It uses Python to define workflows as Directed Acyclic Graphs (DAGs), allowing users to easily schedule, monitor, and manage complex data pipelines. Airflow’s key strengths include:

• Massive ecosystem of plugins and integrations
• Cloud-native design for easy connection to various services
• Web-based UI for workflow visualization and management
• Highly flexible and customizable

Dagster

Dagster, a newer entrant in the workflow management space, was developed to address some of the limitations of early Airflow versions. It focuses heavily on data quality, testing, and analytics. While also using DAGs and a Python-based API, Dagster’s approach differs in several key areas:

• Built-in data quality checks at each pipeline step
• Strong focus on testing and debugging workflows
• Asset-centric approach to data pipelines

Key Differences

1. Workflow Focus

Airflow

Airflow excels at managing complex, branching workflows with conditional logic. It’s designed to handle intricate business logic and automate tasks that data engineers might otherwise do manually across multiple systems. These can be more generic, non data-related tasks. Airflow allows you to:

• Create advanced DAGs with numerous branching possibilities
• Implement conditional workflows based on various factors (e.g., day of the week, data conditions)
• Automate and orchestrate tasks across your entire data stack

Dagster

Dagster’s workflow focus is more centered around data collection, processing, and visualization. It’s particularly well-suited for analytics-focused tasks. Dagster workflows typically involve:

• Collecting data from APIs
• Processing and transforming data
• Visualizing results
• Emphasizing metadata and data source information

2. Data Quality and Testing

Airflow

While Airflow doesn’t have built-in data quality checks, its modular nature allows integration with external tools:

• Can leverage tools like Great Expectations for data quality checks
• Requires manual implementation of quality checks within DAGs
• Offers flexibility to choose and implement preferred testing frameworks

Dagster

Dagster places a strong emphasis on data quality and testing:

• Built-in capability to include data quality checks within DAGs
• Automated testing framework for debugging workflows
• Provides detailed information on step success/failure and causes of errors

3. Community Support and Ecosystem

Airflow

Airflow boasts a massive and growing community:

• Over 10 million downloads per month
• 30 million package downloads by provider monthly
• Thousands of providers and integrations available
• Large, active community (30,000+ members in Slack)

Dagster

As a newer, proprietary solution, Dagster has a smaller but growing community:

• Exact download numbers not published
• Approximately 250,000 monthly website visits
• Over 3,000 community members across various organizations

4. Language and Coding Approach

Airflow

Airflow is purely Python-based:

• DAGs and workflows defined in Python
• Can incorporate SQL, Bash commands, etc., through operators
• Offers granular control over task logic and data passing between tasks

Dagster

Dagster uses a Python-based API:

• Workflows built around data assets
• Heavy use of decorators and API calls
• Focus on orchestrating Python functions for data processing

When to Choose Airflow or Dagster

Consider Airflow if:

• You need a highly flexible and customizable workflow management system
• Your use cases involve complex, branching workflows with conditional logic
• You want to leverage a vast ecosystem of plugins and integrations
• You need to orchestrate tasks across multiple systems in your data stack

Consider Dagster if:

• Your primary focus is on data quality and testing throughout the pipeline
• You’re mainly working with data collection, processing, and visualization tasks
• You prefer a more structured approach to defining data assets and their relationships
• You want built-in testing and debugging capabilities

Conclusion

Both Airflow and Dagster are powerful tools for data orchestration, but they cater to different needs and preferences. Airflow’s flexibility, extensive ecosystem, and ability to handle complex workflows make it a solid choice for many teams, especially those dealing with intricate data pipelines across multiple systems. Dagster’s focus on data quality, built-in testing, and analytics-centric approach make it appealing for teams prioritizing these aspects in their data workflows.