Dagster vs. Airflow: A data orchestration comparison

Modern, asset-centric orchestration or mature, task-based workflows? That’s the choice teams face when evaluating Dagster vs. Airflow.

The data orchestration landscape is evolving fast. Airflow remains a trusted option, while innovative, data-aware tools are pushing the boundaries, making this comparison more relevant than ever.

In this article, we’ll compare key differences between Apache Airflow and Dagster, help you determine which platform is right for your team, and show how pairing your choice with Fivetran can create reliable, end-to-end data pipelines.

What is Dagster?

Dagster is a cloud-native, open-source data orchestration platform that designs, tests, and observes data assets (such as tables, machine learning models, and reports) as well as AI workflows throughout the software development lifecycle.

As a code-first platform, it offers various key features, such as:

• Cloud-native data orchestrator: Dagster develops and monitors data assets throughout their lifecycle using a “data-aware” model, which makes orchestration proactive rather than reactive.
• Development and testing environment: The platform enables local development, testing, and debugging to run and test pipelines before engineers deploy them for production, ensuring higher reliability.
• Declarative scheduling and partitioning: It supports automatic management of complex, time-partitioned data workflows and provides built-in tools for this purpose.

Unlike traditional schedulers that focus on individual tasks, Dagster focuses on the assets being created, treating data pipelines as software with built-in testing and versioning. 

What is Airflow?

Apache Airflow (commonly known as Airflow) is the industry standard for orchestrating and managing ETL/ELT pipelines and other complex workflows. It’s an open-source platform and uses direct acyclic graphs (DAGs) in Python to organize and control task execution.

Its key features include:

• Dynamic DAG generation: Airflow defines data pipelines as DAGs of tasks and generates them from a single template, which accelerates automation and scalability.
• Rich user interface: The platform provides a real-time, browser-based command center that allows users to manage and debug DAGs efficiently.
• Pre-built operator library: Airflow includes a specialized wrapper library that enables orchestration of complex data pipelines without writing custom code for every task.

Because Airflow structures pipelines as DAGs, it’s highly flexible. Still, this task-centric design makes it best suited for general-purpose workflow automation rather than asset-focused data orchestration.

Airflow vs. Dagster: 6 key differences

Both Dagster and Apache Airflow share a common goal: automate, visualize, and coordinate data tasks and their dependencies. However, they take different approaches, and Dagster addresses some areas where Airflow has limitations.

Here are six key differences to consider when evaluating Airflow vs. Dagster.

1. User interface

Airflow uses a task-centric, branching data workflow model that focuses on implementing conditional logic and monitoring the real-time status of individual tasks. 

Dagster, on the other hand, takes an asset-centric approach, providing a holistic, end-to-end view of the data lifecycle. Its interface offers a detailed view of data lineage, showing how assets are connected and how changes in upstream assets impact downstream results.

2. Data quality and testing

Dagster is considered one of the top Airflow alternatives due to its data quality and testing.

Airflow doesn’t have built-in data quality checks. Because it focuses primarily on the execution of tasks rather than the data assets those tasks produce, it provides limited visibility into data lineage and the dependencies between different assets. This can potentially lead to consistency and integrity issues.

Dagster takes a different approach. It has native capabilities to run data quality checks directly within DAGs using Python or integrations with tools like dbt, Soda, and Great Expectations. You can then attach quality test results to the data assets for reporting. Dagster also offers automated testing for debugging workflows, ensuring higher data reliability throughout the pipeline.

3. Architecture

Apache Airflow is built for task-based workflows, and its components reflect that design.

It has a central scheduler, web server, and database, while tasks are executed by workers (such as Celery and Kubernetes) that pull jobs from a queue. Its extensive operator library makes it easier to integrate data with third-party tools. 

As the industry standard, Airflow has a massive ecosystem and connected user community for support.

While Airflow handles general workflows, Dagster puts an emphasis on stateful, data-intensive pipelines. It’s designed for local development and testing and supports native isolation of jobs using Kubernetes pods.

Dagster also organizes tasks within graphs to compute assets, making it particularly effective for complex, asset-centric workflows.

4. Coding approach

Airflow is entirely Python-based. Both DAGs and tasks are defined in Python, giving developers granular control over task logic and the flow of data between tasks.

Dagster also uses a Python-based application programming interface (API), but its workflows revolve around data assets. It relies heavily on decorators and API calls to orchestrate Python functions for data processing.

The coding focus comes down to this: Airflow asks, “What task am I running?” while Dagster asks, “What data am I producing?”

5. Pricing

Pricing highlights a clear distinction between Airflow and Dagster: pay for management or pay for infrastructure. Both platforms have a free, open-source version, but with Airflow, you usually cover your own infrastructure costs, such as Kubernetes or Amazon Web Services (AWS) EC2. 

Managed Airflow services add extra costs, often including a 10–30% markup on compute, which can increase overall operational expenses.

With Dagster, pricing is predictable and based on credits and number of users. It offers three plans: 

• Solo Plan: Starts at $10 per month and is ideal for individual users.
• Starter Plan: Starts at $100 per month and works well for small teams.
• Pro Plan: Custom-priced for larger teams or enterprise needs.

This tiered approach makes Dagster easier to budget for while providing managed infrastructure and dedicated support.

6. Support

As it’s a well-established product, Apache Airflow has a large, mature support community with an extensive plugin ecosystem. In addition to GitHub discussions, Airflow has a Slack community with more than 30,000 members helping one another.

Dagster is a newer solution, so the support community is smaller but growing. The open-source Dagster community has more than 15,000 members in its Slack channel, and GitHub members are frequently active in discussions. 

Airflow vs. Dagster: Which should you choose?

Whether you’re looking for Airflow alternatives or researching what Dagster offers, it’s best to have the full picture before committing. 

Below is a comparison table based on the six key differences between Apache Airflow and Dagster to help make your decision.

Bottom line: Choose Apache Airflow for mature, task-based ETL workflows requiring an extensive plugin ecosystem. Choose Dagster for modern, data-first pipelines focusing on local testing, asset tracking, and full data lineage.

Build reliable data pipelines with Fivetran

Orchestration tools like Dagster and Airflow excel at managing workflow execution, but they rely on a strong data foundation to perform effectively. 

That’s where Fivetran comes in. Fivetran Transformations enables you to automate data cleaning and modeling directly within the destination, so there’s less overhead and more high-quality, reliable data. 

It works seamlessly with data validation and orchestration tools, such as Dagster and Airflow, to create end-to-end pipelines. Whatever tool you use to trigger downstream jobs, Fivetran handles secure, maintenance-free data movement for you.

Start your free trial with Fivetran today.

FAQs

What orchestration tools are similar to Airflow?

Top orchestration tools similar to Apache Airflow include Prefect, Dagster, Kestra, Mage AI, and AWS Step Functions. These tools focus on building, scheduling, and monitoring data workflows as well as dependency management.

Is Dagster production ready?

Yes, Dagster is fully production-ready and widely used to orchestrate complex, data-intensive pipelines, including AI and machine learning workflows. Podman Quadlets allow containerized applications to run without requiring Kubernetes or other complex solutions.

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