Apache Airflow 3.0 has officially landed, and it represents a substantial evolution of the platform since its inception. This release is not just a collection of incremental changes, it’s a rethinking of how workflows can and should be managed at scale. Packed with performance enhancements, quality-of-life improvements, and forward-looking features, Airflow 3.0 aims to address many of the longstanding frustrations within the data engineering community while keeping pace with modern orchestration needs. However, there’s still room for refinement. Deployment remains clunky, the developer experience could be more intuitive, and the documentation has yet to fully catch up with the new features, leaving gaps in usability. Despite these challenges, Airflow 3.0 lays a strong foundation for future innovation.
A New Foundation for Workflow Orchestration
The biggest additions of Airflow 3.0 at a glance:
One of the most anticipated additions to Airflow 3.0 is its support for event-driven scheduling. Previously, Airflow’s strength was in time-based, cron-style orchestration. While this model works well for batch pipelines, it struggles in scenarios where real-time responsiveness is critical. With 3.0, workflows can now respond to data events, such as files appearing in cloud storage buckets or updates occurring in databases, enabling near-real-time orchestration. This positions Airflow to handle streaming and micro-batch use cases more elegantly than ever before (DataCamp).
Another major advancement is built-in DAG versioning, which allows every execution of a Directed Acyclic Graph (DAG) to be tied to a specific, immutable snapshot of its definition. This feature significantly improves debugging, traceability, and auditing, particularly for organizations in regulated industries where compliance and reproducibility are crucial. The versioning helps answer the critical question of what code is executed at which point in time.
Airflow 3.0 also comes with a completely overhauled web UI, rebuilt using modern frontend technologies. The new interface delivers faster performance and a cleaner user experience. With improved tools for visualizing DAG runs, managing tasks, and inspecting logs, the user interface is no longer a pain point, but a productivity enhancer.
Furthermore, the new asset-centric syntax enables developers to use the @asset decorator to define workflows directly around data assets. This reduces boilerplate and aligns pipeline logic more naturally with the data itself. In practice, it shifts the paradigm from orchestrating tasks to orchestrating data—a subtle but meaningful conceptual leap.
Lastly, scheduler-managed backfills eliminate the hassle of managing historical data reprocessing through fragile CLI commands. Backfills can now be triggered, paused, and monitored directly from the UI or API, dramatically simplifying historical data correction.
Apart from newly added features, Airflow 3.0 also brings some improvements under the hood. Astronomer.io, a key driving force behind Airflow’s ongoing development, played a crucial role in the rollout of Airflow 3.0. As detailed in their overview, the most substantial optimizations include:
A Faster Scheduler: The Airflow 3.0 scheduler is optimized for speed and scalability, reducing latency during DAG processing and enabling faster task execution feedback.
Active Dependency Management: Improved dependency tracking increases responsiveness and execution efficiency, particularly for complex pipelines.
Database Connectivity Improvements: Airflow now interacts with metadata databases more efficiently, improving stability and reducing load on the backend.
Upgrade Path Enhancements: Astronomer has simplified the upgrade path for both self-hosted and managed Airflow environments, making it easier for organizations to move from previous versions to 3.0.
Advantages That Elevate the Experience
The improvements in Airflow 3.0 offer several benefits. Not only the changes in the architecture, but also the updated UI enhance observability and performance, giving engineers better insight into the health of their workflows through logs, metrics, and task statuses.
Airflow’s maturity continues to be one of its biggest strengths. With wide adoption across industries and robust community support, organizations can confidently deploy and scale their pipelines using a platform with proven reliability. Integration with other tools, like dbt (data build tool), remains strong, allowing users to orchestrate transformations seamlessly in concert with data ingestion and extraction workflows (see: Execute DBT with Airflow and Cloud Run).
Pain Points That Still Remain
Despite the significant improvements introduced in Airflow 3.0, the platform still carries several challenges that may hinder its adoption and use. One major issue is its steep learning curve, which remains a roadblock for many teams. Configuring DAGs, setting up deployment infrastructure, and troubleshooting failures often require deep technical expertise and careful coordination between multiple components.
Running Airflow at scale continues to be a resource-intensive undertaking, particularly for organizations managing hundreds or thousands of orchestrated tasks per day. Additionally, while Airflow 3.0 has taken strides forward, it still doesn’t fully embrace streaming workflows yet.
Historically, Airflow has faced criticisms related to scheduler instability, DAG deadlocks, and challenges with local testing—problems often discussed in forums like this Reddit thread. Although some of these issues have been alleviated in version 3.0, others persist and remain in areas that require further focus and improvement.
Challenges Specific to Airflow 3.0
While Airflow 3.0 introduces several new features, many are underdeveloped and fall short of being ready for seamless use. For instance, the implementation of Asset adapters and Event-based triggers appears incomplete. These much-anticipated features lack polish for seamless developer experience, by only supporting a limited amount of connectors right out of the box. Similarly, the UI for visualizing Assets and DAGs can be confusing, leaving users struggling to fully understand or manage the relationships between these components.
Another pain point relates to DAG versioning, which is unable to effectively track changes to dbt models using cosmos - a capability that many may find invaluable.
The overall developer experience in Airflow 3.0 also continues to feel clunky. From initial setup to debugging, developers starting out with Airflow may struggle with the platform’s tools and processes. This is coupled with the complex nature of deployments, which are especially challenging in advanced scenarios, such as setting up Airflow on Kubernetes or managing hybrid-cloud environments.
One of the most noticeable drawbacks of Airflow 3.0 is the insufficient or missing documentation for its new features. Teams are left without clear guidance on several critical areas, such as:
How to connect Assets with classic DAGs.
How to implement and use Asset adapters.
How to configure and utilize Event-based triggers effectively.
This lack of comprehensive documentation creates a barrier for teams trying to explore and adopt the new functionalities introduced in Airflow 3.0. Without practical examples or detailed tutorials, understanding the usage of these features from the source code can be cumbersome.
In the rapidly evolving world of orchestration tools, how does Airflow 3.0 stack up?
Prefect has long marketed itself as a more Pythonic and lightweight alternative to Airflow. It offers seamless local development, easy debugging, and a simple function-based interface. For teams seeking quick setup and modern developer experience, Prefect is hard to beat. However, Prefect may fall short in highly complex enterprise environments where Airflow’s fine-grained scheduling and extensibility are still unmatched. In comparison, Prefect is easier to use, Airflow’s integrations and broader community support make it more suitable for complex, regulated workflows.
Dagster, by contrast, places a heavy emphasis on data assets and observability. It provides a more modular, testable, and development-friendly interface for building pipelines. Dagster’s partitioning mechanism, better support for local development, and clear delineation between production and staging make it a favorite among data teams with engineering-heavy workflows. Airflow 3.0 narrows this gap considerably with its asset-centric features and improved developer tooling, but Dagster still feels more modern in its architecture. Still, the smaller community and the missing RBAC support for the open source version might be a deal breaker for some projects.
Other tools like Kestra, Shipyard, and DataChannel each have their own niches, often targeting ease-of-use or native SaaS integrations, but they don’t yet match Airflow’s flexibility. Azure Data Factory (ADF) offers strong native integration with the Azure ecosystem and is ideal for Microsoft-heavy shops, though it lacks the open-source extensibility that defines Airflow.
Final Thoughts
Apache Airflow 3.0 is a big release that reaffirms the platform’s relevance and adaptability. It addresses many of the performance, usability, and reliability issues that have long plagued the system, while introducing forward-looking features that make it competitive with modern orchestrators like Prefect and Dagster.
While the platform still demands operational expertise and thoughtful architecture to run effectively, it now offers a significantly better out-of-the-box experience. For organizations that need to scale complex workflows with a high degree of control and visibility, Airflow 3.0 offers one of the most mature and capable orchestration solutions on the market today.
As mentioned, there are areas that still need attention from the Airflow community. The immaturity of new features, the incomplete documentation, and the ongoing challenges with deployment complexity and usability can create frustration for teams. For organizations already invested in Airflow, these might be manageable issues, but for those evaluating orchestration tools, they could serve as deterrents. In some cases, tools like Dagster or Prefect, with their more intuitive workflows and seamless integrations, may still be more attractive alternatives. Airflow 3.0 has set a strong foundation for future growth, but there is still work to be done to make it a truly developer-friendly and scalable solution.
