Dagster vs. Airflow

We often get asked why data engineering teams should choose Dagster over Airflow. It boils down to a few key differences:

• Foundation for your Data Platform: Dagster focuses on data assets, not just tasks, giving you better visibility into data lineage and dependencies. Since Dagster understands the state and lineage of your data assets, its the ideal tool for observation and operational concerns.
• Principled architecture: Designed from the ground up for modern data workflows, Dagster has better local development, testing, and debugging.
• Full data engineering lifecycle management: Dagster supports the entire data lifecycle from development to production with built-in support for CI/CD and observation tools.

This post will discuss how Dagster addresses Airflow’s limitations and why it’s better for data teams looking to build more powerful data platforms.

We will also review how to bring your existing Airflow DAGs into Dagster without modifying your underlying code.

The Origins of Apache Airflow

Apache Airflow was one of the first data orchestration tools. It was created in 2014 to address the need for an efficient, programmable, and user-friendly way to schedule and execute complex data tasks. Airflow solved the problems of that time by providing a way to manage and schedule tasks.

Airflow is a big step forward from the old manual and error-prone ways of managing data pipelines. Its Python-based design makes it accessible to many users, as Python is one of the most popular languages in data science and engineering.

Airflow is still popular today because of its mature ecosystem and broad adoption.

Apache Airflow Pros and Cons

Airflow’s initial design changed data task management, but it is a vestige of a bygone era in many ways. It has brought us a long way, but it doesn't quite fit in the modern world, where your orchestrator must be flexible enough to align with your organization's functions.

Airflow works well within its scope, which is:

• Managing and executing task-based workflows
• Connecting to various data sources and services through its plugins and integrations
• Simple pipelines without complex data dependencies or asset management

But Airflow falls short in many areas needed for efficient data operations today:

Local Development and Testing

Local development and testing help find and fix issues early in development. Airflow’s architecture makes this harder because tasks are often tightly coupled to the production environment, making it difficult to replicate the exact conditions locally. Airflow’s dependency management can cause conflicts and make local setup harder. So issues are sometimes not found until staging or production environments. By then, fixes are usually more expensive and time-consuming.

Debugging

Debugging is a critical component of data engineering, ensuring transformations function correctly and issues are identified and resolved promptly. However, Airflow's workflow debugging presents significant challenges due to its APIs. The platform suffers from inadequate observability and a less robust metadata system for reporting, making it difficult for engineers to determine failure points rapidly. Logs are unstructured and fragmented across different tasks, obscuring the root causes of issues.

Additionally, the UI fails to provide a comprehensive view of the data platform, further complicating the debugging process. These limitations lead to tedious troubleshooting, delayed error identification, and extended resolution times—ultimately forcing engineers to spend more time firefighting than delivering value through innovation.

Data Lineage and Asset Management

Understanding data lineage and dependencies helps you manage complex data flows and see the impact of your changes. Airflow’s retroactive focus on datasets results in an inferior implementation, so tracking data lineage and understanding dependencies between different data assets is hard. Lack of visibility means low data quality, consistency, and potential data integrity issues.

Scalability and Isolation

Scalability is important because it lets your environment handle increased loads as it grows. Isolation prevents tasks from interfering with each other. Airflow’s monolithic architecture can cause scalability issues because all tasks share the same environment, which can cause performance bottlenecks. An error in one task can affect others. In large environments, this can mean performance degradation, increased risk of failures, and harder-to-isolate tasks to prevent interference.

CI/CD

Continuous Integration and Continuous Deployment (CI/CD) practices enable more efficient and reliable software development through automated testing and deployment. However, automated testing and deployment are harder to implement in Airflow because tasks are tightly coupled to the environment they run in. Thus, creating isolated, repeatable test environments is challenging. This slows the development cycle and increases the risk of introducing bugs into production, making it harder to maintain high-quality software.

Airflow has let data engineers get stuff done – but often with much heartache. The limitations become more apparent as data environments become more complex and evolve. Teams find themselves with late-stage error detection in production, outdated data, inflexible environments, and dependency management that grinds new data product releases to a halt.

Closing the Gap

We must move towards more advanced tools that fix these issues and have stronger foundations than Airflow for data orchestration.

Organizations need an orchestration solution to close these gaps beyond task execution. This solution must manage and optimize data assets—tables, files, and machine learning models—across the entire lifecycle. It must also integrate seamlessly with modern development practices, from local testing to production deployments, all backed by cloud-native capabilities.

This is part of Dagster's reality: agile and transparent operations while controlling and shipping data fast and efficiently.

Enter Dagster

Dagster is a new paradigm in building data platforms. Dagster was designed for the evolving needs of data engineers. Unlike its predecessors, Dagster was built from the ground up with data assets and the full development lifecycle in mind for a more complete and integrated approach to data pipelines.

Dagster's focus on Assets to quickly answer questions :

• Is this asset up-to-date?
• What do I need to run to refresh this asset?
• When will this asset be updated next?
• What code and data were used to generate this asset?
• After pushing a change, what assets need to be updated?

Dagster provides data teams with a more ergonomic experience for easily defining, testing, and running their pipelines locally, on staging, and in production. We also focus on developer productivity with rich, structured logging and a web-based interface to give them end-to-end visibility and control over data pipelines.

Dagster vs. Airflow

Dagster easily addresses the areas where Airflow falls short. Here’s how:

Organizational Benefits

Beyond technical advantages, asset-based orchestration offers organizational benefits that are particularly valuable for data teams:

1. Simplified Onboarding

Despite the initial learning curve, asset-based systems often prove easier for new team members to understand once they grasp the core concepts. The direct mapping between code and data assets creates a more intuitive mental model for how you actually talk about Data.

2. Improved Collaboration

Asset-based approaches facilitate more straightforward communication between team members. This modular approach enables teams to work on different parts of the data platform without stepping on each other's toes.

3. Better Documentation through Code

When data assets are explicitly defined and connected, the code itself becomes a form of documentation. This self-documenting nature reduces the need for external documentation and helps keep technical specs in sync with the actual implementation.

Local Development and Testing

Dagster is built with local development and testing in mind. For example, you can run your entire pipeline locally with the dagster dev command. This command starts a local Dagster instance where you can define, test, and run your workflows without external dependencies. This local development environment speeds up iteration and helps catch errors early, making development faster, more efficient, and less error-prone.

Debugging

Dagster gives rich, structured logs and a local development environment to debug pipelines through its UI. If a pipeline fails, you can navigate through the logs with Dagster’s UI to find the exact step and error. The structured logs include metadata so you can understand the context of each log entry.

Data Lineage and Asset Management

Dagster takes a data-first approach to workflows, giving you complete visibility into data lineage and dependencies. For example, if you have a data pipeline that processes raw data into cleaned data and then into a report, Dagster can show you the entire lineage from raw data to the report. This visibility helps you manage complex data flows and understand the impact of changes.

Scalability and Isolation

Dagster is designed to be highly scalable and handle large and complex data workflows. Additionally, Dagster’s architecture supports scalable execution environments like Kubernetes. You can define resource requirements for each job or step so tasks run in isolated environments.

For example a CPU bound task can be scheduled on a node with high CPU availability and an I/O bound task can be scheduled on a node optimized for I/O.

CI/CD

Dagster has built-in CI/CD, so you can implement these best practices and automate testing and deployment. For example, Dagster+ has built-in GitHub Actions to automate the packaging and deployment of your code. You can set up a CI/CD pipeline that runs tests on your Dagster pipelines every time you push to your GitHub repository, so your code is always tested and deployed consistently.

Dagster+

In addition to Dagster’s core features, Dagster+ adds a lot to Dagster's ability to compete with the best in DataOps. It builds on top of Dagster’s foundation and adds more features for enterprise-scale data operations:

Data Asset Management

Dagster+ has a built-in data catalog that captures and curates metadata for your data assets. This catalog gives you a real-time, actionable view of your data ecosystem, showing column-level lineage, usage stats, and operational history. This makes it easier to discover and understand data assets.

Operational Workflows

Dagster+ has advanced operational workflows that streamline the entire data orchestration lifecycle. These workflows introduce automated data quality checks, alerting, and incident management. For example, if a data quality check fails, Dagster+ can automatically send an alert and create an incident ticket so you can get attention to issues immediately.

Security and Compliance

Data governance and compliance are vital in Dagster+. Features like role-based access control (RBAC), audit logs, and data encryption ensure data protection and compliance. You can also define fine-grained access controls to limit who can view or modify specific data assets.

Priority Support

Dagster+ subscribers get priority support, so issues get resolved quickly and data pipelines run smoothly. For example, if you have a critical issue in production, you can contact the Dagster support team and get expedited help.

Context Rich View of Assets

Dagster+ gives you a full context-rich view of data assets, including metadata about asset generation, usage, dependencies, and status changes. You can see an asset's full history, including when it was last updated, who updated it, and any changes to the schema.

Platform Integration with External Assets

Dagster+ extends its cataloging to external assets, so all your key assets and metadata are in one place. For example, if you have data assets managed by external systems like Snowflake or BigQuery, Dagster+ can integrate them with those systems to give you a unified view of all your data assets.

Searchability and Discoverability

Dagster+ makes data assets more searchable and discoverable so teams can find what they need. This improves data operations and your overall data management strategy.

Using Airflow with Dagster

You might have a large Airflow installation in your organization that’s not going away anytime soon.

In some cases, Dagster and Airflow sit side by side and independently of each other. Instead of going through a lengthy migration process, you can get day one value with Airlift.

Final Thoughts

Apache Airflow is used to build and run data pipelines but wasn’t designed with a holistic view of what it takes to do so. Dagster was designed to help data teams build and run data pipelines: to develop data assets and keep them up to date. The impact on development velocity and production reliability is enormous.

So give it a try Today!

We're always happy to hear your feedback, so please reach out to us! If you have any questions, ask them in the Dagster community Slack (join here!) or start a Github discussion. If you run into any bugs, let us know with a Github issue. And if you're interested in working with us, check out our open roles!

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