Airflow Multi-Tenancy: Key Considerations and Strategies

Apache Airflow® has emerged as the de facto standard for orchestrating data workflows and building data products. As organizations expand their data initiatives—from ETL processes to MLOps and data-driven apps—the use of Airflow has grown exponentially. However, as teams scale their Airflow usage, many segregate different teams into separate Airflow environments. This is because implementing multi-tenancy in Airflow comes with challenges.

In this article, we’ll discuss these challenges and introduce Astro, the managed Airflow service from Astronomer, that provides a robust and secure approach to Airflow multi-tenancy.

What is Airflow Multi-Tenancy?

Airflow multi-tenancy refers to the capability of an Apache Airflow deployment to serve multiple users or teams (tenants) while keeping their data pipelines, configurations, and resources isolated from one another. This approach optimizes resource utilization and reduces operational costs by eliminating the need for separate Airflow instances for each team.

Why Do Organizations Want Airflow Multi-Tenancy?

• Resource Optimization: Consolidate infrastructure to reduce costs and improve efficiency.
• Cost Efficiency: Lower overhead by sharing resources without compromising performance.
• Isolated Workloads: Ensure that one team’s processes do not interfere with another’s.
• Centralized Management: Simplify administration by managing all tenants from a single platform.

Challenges with Airflow Multi-Tenancy

Implementing multi-tenancy with Airflow can be challenging due to executional control versus permissions control, noisy neighbors, upgrades, and operational overhead.

Execution Control vs Access Control

Out of the box today, Airflow supports DAG-level Role-Based Access Control (RBAC) for controlling which users can see what DAGs and the actions they can take on them. This can be helpful in following principles of least privileged access, but ultimately isn’t enough for true multi-tenancy.

While DAG-level RBAC is helpful for controlling access, it can create a false sense of security. Users can write DAGs that directly access the underlying database, potentially altering user privileges. Moreover, RBAC controls only what a user can see, not the underlying resources like connections and variables.

While additional overhead can mitigate these issues through rigorous CI/CD and platform engineering, this approach often leads to technical debt and slower upgrade cycles. It also places a significant burden on platform teams during downtimes or unexpected failures, as end users might lack access to necessary debugging views.

Noisy Neighbors

When sharing an environment, the actions of one team may inadvertently affect another. These “noisy neighbors” situations can result in key datasets and data products missing their associated SLAs. This could be the result of one team consuming all available worker resources during peak loads – if both teams have their most mission critical workloads scheduled for midnight UTC, they might get throttled due to worker availability.

This situation applies not only to worker resources, but also to scheduler availability. Airflow has no mechanism for “schedule priority,” meaning that the most mission-critical pipelines can’t be prioritized through scheduling priorities or resource quotas.

Additionally, many of the configurations required to fine tune Airflow for a specific use case exist at an environment level, not at a DAG level. Two teams may have conflicting configurations around concurrency settings, executors, or others needed for a use case, or different stylistic preferences (default retries, catch up settings, etc.).

Python package conflicts also pose challenges. Data science teams might need specific ML libraries, while data engineers may require different versions of underlying packages. Resolving these conflicts can be cumbersome and hinder productivity – what if two teams want to use different versions of pandas? Or what if folks are trying to use different versions of provider packages? If there’s a conflict that exists, DAG authors may have to bear the burden of refactoring to find a solution (switch to something like KubernetesPodOperators) or manually create workarounds .

Upgrades

Coordinating upgrades is a lot harder if multiple teams have to be in sync for the upgrade to be executed. Scheduling any sort of downtime or maintenance window could cause disruption across multiple teams, making it harder to adopt new features being released in Airflow.

While upgrades may only happen every quarter, this same problem can apply to coordinating different repos. Teams are either left maintaining several different CI/CD scripts, or left to fit all their DAGs within one repo, both of which carry undifferentiated maintenance costs.

Running Multiple Airflows is a Full Time Job

Today many teams opt into running a monolithic environment not because it’s optimal, but because of constrained DevOps or DataOps resources. Maintaining even one environment demands effort. Scaling this to provide multiple teams with development, staging, and production environments is a substantial undertaking.

Infrastructure costs also add up— despite Airflow’s relatively light footprint— due to the compute requirements of long-running services like the scheduler and web server.

Allowing teams to spin up multiple Airflow environments can create observability issues. “Airflow sprawl” can result in key datasets being managed by outdated or insecure setups. Centralized logging, monitoring, and governance become essential, especially when one team’s DAGs serve as inputs for another.

Astro: Simplifying Airflow Multi-Tenancy

When data teams are looking to run multi-tenant Airflow, they’re looking to balance the speed of letting teams move at the pace they want to, with the flexibility to meet users where they are at, without losing the isolation and observability that mission critical data workloads require. As such, Astro is designed to give all engineering teams an easy path to production for their data pipelines.

Isolated Environments with Deep Visibility.

The Astro Control plane provides a single point of control and governance for creating Airflow environments. These environments can all run in separate clusters and cloud providers, integrating with your identity provider for seamless permissions management. The control plane serves as a single pane of glass. Users see only the DAGs they have access to, regardless of where they run.

Administrators benefit from dashboards displaying:

• Location, version and underlying productivity metrics of each environment (tasks run, number of code deploys, etc.)
• SLAs defined and missed for the underlying DAGs.
• Breakdown of costs associated with each environment.
• Operator usage over time.

Enhancing Multi-Tenancy with Astro Observe

Astro Observe provides even deeper visibility and control for multi-tenant environments. With pipeline-level data observability, Astro Observe provides a comprehensive view of your Airflow environment; with tracking across the health and performance of data products and data pipelines to ensure they meet business goals.

The SLA dashboard lets teams set data freshness and delivery thresholds, and provides real-time alerts when issues arise. With full lineage tracking, teams can quickly pinpoint activity and ownership across even the most complex workflows, ensuring swift remediation when needed.

Ephemeral, Hibernating, and Autoscaling Environments

One of the reasons why observability is so important is that Airflow environments can be very different from one another. Some use cases require different compute needs, while others are only experimental and don’t need a permanent underlying environment. For these short lived development environments, “branch based deploys” brings a web development-like development experience to data engineering. The lifecycle of these ephemeral development environments are matched to the branch they’re connected to.

Additionally, not only do Airflow workers scale to 0 when they’re not being used, but environments can be scheduled to scale down all Airflow components to 0 when they’re not being used — what we call “Hibernation.” Each environment can follow its own hibernation schedule, making the best use of infrastructure. These can be used in conjunction with the ephemeral environments to ensure that infrastructure is only running when it is being used. The automation required to standardize this within an enterprise can be done directly via the astro-cli, through a fully functional REST API, or through the official Astronomer Terraform provider.

Rollbacks for Easy Upgrades, and Safety

Last but not least, despite the decreased risk that environment isolation affords, we know that unexpected situations do occur. Astro provides the ability to rollback an environment to a previously healthy state. This not only adds to the safety of trying new things (a new version of Airflow, an updated provider package, etc.), but also allows for easier resolution of outages.

Airflow Multi-Tenancy Use Cases

Organizations across various industries are leveraging Astro for their multi-tenant Airflow deployments:

Departmental Isolation

Separate data pipelines for various departments (e.g., finance, marketing, operations) within an organization to maintain compliance and streamline workflows.

Data Science Teams

Enable multiple data science teams to work independently with isolated resources, ensuring that experiments and pipelines do not conflict with one another.

Customer Segmentation

Provide dedicated Airflow environments for different customer segments, allowing for customized data processing and enhanced security.

Getting Started with Airflow Multi-Tenancy on Astro

Ready to simplify your Airflow multi-tenancy implementation? Astro offers an intuitive platform to get you started quickly.

Take the interactive Astro product tour to explore Astro’s features and see how it can transform your data pipeline management. If you want to dive right in, get started for free by signing up for a free Astro trial and experience the benefits of Astro firsthand.

Frequently Asked Questions about Airflow Multi-Tenancy

What is Airflow multi-tenancy?

Airflow multi-tenancy allows Apache Airflow to serve multiple users or teams while keeping their pipelines and resources isolated. This optimizes resource use and reduces the need for separate environments.

Why is Airflow multi-tenancy important?

It enables resource optimization, cost efficiency, and isolated workloads while simplifying centralized management across teams, ensuring efficient and scalable operations.

What are some of the key challenges of implementing multi-tenancy in Airflow?

Key challenges include:

• Execution vs. Access Control: Airflow’s DAG-level RBAC controls what users can see but doesn't isolate underlying resources, leading to potential security risks.

• Noisy Neighbors: Teams may interfere with each other, consuming shared resources or causing scheduling conflicts.

• Upgrades: Coordinating upgrades across multiple teams is difficult, often delaying adoption of new features.

• Operational Overhead: Managing separate environments or repos increases complexity and maintenance costs for platform teams.

How does Astro achieve Airflow multi-tenancy?

Astro addresses Airflow’s multi-tenancy challenges by providing isolated environments with centralized governance through a control plane. It resolves execution vs. access control issues with secure permissions management, minimizes “noisy neighbor” problems through resource visibility, and allows individual teams to manage workloads without conflicts. Additionally, Astro simplifies upgrades by enabling rollback and provides automated scaling to reduce operational overhead.