Apache Airflow vs. Dagster

Summary

The transcript is about Dagster, a data orchestration tool aimed at addressing the limitations of the widely-used Airflow. The speaker, a lead engineer on the Dagster project, explains how Dagster's approach differs from Airflow's. Dagster focuses on producing and maintaining data assets, supports rapid development and testing, and offers an asset-centric programming model and user interface. The speaker highlights Dagster's advantages, such as improved developer productivity, reliability, and visibility into data assets, as well as its deeper integrations with modern data stack tools like DBT. The comparison with Airflow illustrates how Dagster's design principles, centered around modern software engineering practices, make it a powerful alternative for data pipeline development and management.

Takeaways

• 👨‍💻 The speaker is the lead engineer on the Dagster project and previously worked as a data engineer and machine learning engineer using Airflow extensively.
• 😫 The speaker joined the Dagster project due to frustrations with Airflow, where more time was spent fighting with it rather than writing actual data pipelines.
• 🔄 Data practitioners use orchestrators like Dagster or Airflow to build and run data pipelines, which produce and maintain data assets like tables, files, or machine learning models.
• 🤖 Airflow is designed as a workflow engine that models and executes a graph of tasks on a fixed schedule, but it takes a narrow view of data pipelines and misses the bigger picture of what modern data teams are trying to accomplish.
• 📈 Dagster takes a broader view and was designed to assist with the holistic task of developing pipelines of data assets and evolving those pipelines over time.
• ⚡ Dagster supports rapid development and prototyping of data pipelines, separating business logic from infrastructure, allowing pipelines to run locally or in continuous integration without sacrificing dependency isolation.
• 🧱 Dagster's programming model and user interface are heavily focused on the goal of producing data assets, while Airflow is primarily a task orchestrator.
• 📊 Dagster's web UI focuses on the data produced by tasks, making it easy to include metadata and track how data evolves over time.
• 🔗 Dagster enables deeper integrations with modern data stack tools like dbt, representing individual dbt models as assets and making it easy to understand relationships between models and other data assets.
• 🚀 The speaker highlights the differences between Dagster and Airflow in terms of development, abstractions for building and operating data pipelines, and integrations with modern data stack tools.

Q & A

What is the main purpose of data orchestration tools like Dagster and Airflow?

Data orchestration tools like Dagster and Airflow are used to build and run data pipelines. The purpose is typically to produce and maintain a set of data assets like tables, files, or machine learning models.

What is the fundamental difference in how Dagster and Airflow approach data pipelines?

Airflow is primarily a task orchestrator, focused on scheduling tasks based on execution dependencies. Dagster, on the other hand, takes a broader view and is designed to assist with the holistic task of developing pipelines of data assets and evolving those pipelines over time.

How does Dagster facilitate rapid development and prototyping of data pipelines compared to Airflow?

Dagster separates business logic from infrastructure, allowing pipelines to run within a single Python process during development or testing, without sacrificing dependency isolation. It also provides rich testing APIs and lightweight execution without requiring long-running services or schedules.

What are the benefits of Dagster's asset-centric approach compared to Airflow's task-centric approach?

Dagster's asset-centric approach allows you to express your intentions more directly, resulting in less code boilerplate. It also provides better visibility into the data produced by tasks and enables deeper integrations with modern data stack tools like dbt.

How does Dagster's UI differ from Airflow's UI in terms of monitoring data pipelines?

While Airflow's UI is primarily concerned with what tasks ran, Dagster's web UI also focuses on the data that was produced by those tasks. It makes it easy to include metadata about the data and track how it evolves over time.

What are some of the challenges mentioned with using Airflow for developing data pipelines?

The script mentions that with Airflow, iteration cycles are slow when pipelines can only execute in production. It's also cumbersome to translate a pipeline of data assets into scheduled workflows of tasks. Additionally, it's challenging to catch errors before changes make it to production, leading to potential reliability issues.

How does Dagster handle dependency isolation compared to Airflow?

The script mentions that Dagster handles dependency isolation better than Airflow, but it doesn't provide specific details on how this is achieved.

What is the significance of Dagster's integration with dbt?

Dagster can represent the full dbt graph, making it easy to understand the relationships between individual dbt models and other data assets. It also allows running individual dbt models and tracking which models completed successfully.

How does Dagster's approach to event-based execution differ from Airflow?

The script mentions that there are differences between Dagster and Airflow in how they handle event-based execution, but it doesn't provide details on what those differences are.

What challenges does the speaker mention regarding upgrades in Airflow and Dagster?

The script states that there are differences between Dagster and Airflow in how they handle upgrades, but it doesn't provide specific details on those challenges or differences.