Choosing the right data orchestration for modern data platforms
Dagster vs Airflow

When it comes to orchestrating data pipelines there are two names which are often mentioned: Apache Airflow and Dagster.

After our previous blogpost where we discussed what Dagster is, we continue this blog post series with comparing those two data orchestrators.

AUTHOR – Joris

Dagster vs Airflow

As Acumen, we believe that the newer kid on the block, Dagster, is better suited to orchestrate a data platform nowadays.

It boils down to a few key differences:
• Asset orientation: Dagster focuses on data assets and not just tasks which gives a better visibility into data lineage and dependencies between assets and systems and gives Dagster the ideal starting point to be the place to be for managing a data platform.

• Monitoring data operations: With the structured run output offered out of the box with Dagster, you can more easily do local development, testing and debugging.

• Single pane of glass for cross system data platform: Flowing out the asset orientation, Dagster gives a full cockpit overview of the current state of the data platform at every moment.

Pros and Cons
of Airflow

Airflow’s initial design changed data task management, but it was designed in a different era of data engineering: one where the full data lifecycle wasn’t as complex as it is today.

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, testing and debugging: Airflow’s architecture makes it hard to replicate the exact production conditions locally. So issues are sometimes not found until the staging or production environments

• Data Lineage and Asset Management: Understanding data lineage helps you manage complex data flows and see immediately the impact of your changes. The biggest issue is that Airflow focuses on the execution of the tasks rather than the produced data assets which means less visibility

• Scalability: Due to Airflow’s monolithic architecture, all tasks share the same environment which can cause performance bottlenecks and it is harder to isolate tasks to prevent interference

To close these gaps, organizations need an orchestration solution that goes beyond task execution, managing and optimizing data assets – tables, files and machine learning models – across the entire lifecycle.
It also needs to integrate seamlessly with modern development practices, from local testing to production deployments, all backed by cloud-native capabilities. This is part of the reality that Dagster offers: agile and transparent operations while controlling and shipping data fast and efficiently.

“To close the gaps in modern data operations, organizations need an orchestration solution that goes beyond task execution—managing and optimizing data assets across the entire lifecycle.”

Enter Dagster

Dagster is a new paradigm in data orchestration: taking a radically different approach to data orchestration than other tools.

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 asset-oriented nature allows it to easily answer questions such as:
• 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?

With this approach, we give data teams a more straightforward experience, so they can define, test and run their pipelines locally with ease. We also focus on developer productivity with rich, structured logging and a web based interface to give visibility and control over data pipelines.

While Airflow has long been a staple in data orchestration, the evolving complexity of modern data platforms calls for a more asset-focused and scalable approach. Dagster stands out by offering deeper visibility, enhanced testing capabilities, and seamless data lifecycle management. For organizations looking to future-proof their data operations, Dagster provides a powerful solution.

Interested in migrating from Airflow to Dagster? In our next blog post we dive deeper into how we can facilitate the migration process with our step-by-step incremental approach.

Curious about how Dagster can elevate your data platform? As an official Dagster implementation partner, Acumen is here to guide you every step of the way.

Contact us today to explore how we can help you seamlessly transition and unlock the full potential of your data workflows.