Apache Airflow®: The De Facto Standard for Data Workflow Automation.

Apache Airflow is an open-source workflow orchestration tool that automates and schedules complex data pipelines.

In Airflow, data pipelines and workflows are abstracted as directed acyclic graphs, or DAGs. Hundreds of Airflow operators are the building blocks of DAGs, giving you pre-built functionality you can use to simplify common tasks used to build data products, like running SQL queries or interacting with cloud storage and database services. Additionally, Airflow decorators allow you to effortlessly turn any Python script into an Airflow task, providing you with full flexibility to connect to any tool or service that has an API. You can combine operators and decorators to create DAGs that automate complex pipelines — like integrating newly ingested data from different sources for AI model training and fine-tuning, or pre-processing data from dozens of operational systems for building data driven apps or to provide regulatory reports.

Airflow’s built-in logic allows you to focus on making sure your data pipelines are reliable and performant — without worrying about writing custom code to manage complicated dependencies or recover from pipeline failures.

You’d choose Apache Airflow for its flexibility, scalability, and extensive community support. It handles complex workflows with ease, provides pre-built operators to interact with the broadest range of tools (e.g., cloud services, AI frameworks, data processing tools, data warehouses), and enables you to monitor pipeline health and investigate errors in a user-friendly UI. By expressing data flows as intuitive Python code, Airflow can be used across multiple teams composed of data engineers, data analysts and scientists, app developers, and SREs.

Airflow’s open-source nature ensures continuous development and improvement, with the project quickly adapting to changes in the data ecosystem thanks to over 3000 individual contributors to date.

Airflow is the open-source standard for automating repetitive, complex workflows across different environments. Airflow can automate a wide range of tasks, including:

• Data pipelines (ETL/ELT): Extracting, transforming, and loading data.

• Machine learning workflows: Automating training, evaluation, and deployment of models.

• Cloud services: Provisioning and scaling infrastructure.

• API calls: Scheduling interactions between systems.

• Data processing: Running scripts processing data directly in the Airflow worker or on external distributed computing platforms.

• Monitoring and alerting: Automating notifications for failed tasks or data quality issues.

Apache Airflow is a key component in the modern data stack, serving as the orchestration layer that connects the tools data teams use to unlock data value. It automates workflows involving data ingestion, transformation, MLOps and analytics by scheduling tasks and managing dependencies.

Airflow integrates seamlessly with tools like dbt, Snowflake, Databricks, ML/AI frameworks, and cloud services, making it essential for building reliable, scalable data pipelines. Its flexibility and extensibility allow it to work with various data platforms and cloud providers, ensuring efficient end-to-end pipeline management.

Airflow is popular with data professionals as a solution for automating the tedious work involved in creating, managing, and maintaining data pipelines, along with other complex workflows. Data platform architects depend on Airflow-powered workflow management to design modern, cloud-native data platforms, while data team leaders and other managers recognize that Airflow empowers their teams to work more productively and effectively.

Here’s a quick look at how a variety of data professionals use Airflow.

• Airflow and Data Engineers: Data engineers can apply Airflow’s support for thousands of Python libraries and frameworks, as well as the hundreds of pre-built Airflow operators, to create the data pipelines they depend on to acquire, move, and transform data. Airflow’s ability to manage task dependencies and recover from failures allows data engineers to design rock-solid data pipelines.

• Airflow and Data Analysts: Data analysts and analytic engineers depend on Airflow to acquire, move, and transform data for their analysis and modeling tasks, tapping into Airflow’s broad connectivity to data sources and cloud services. With optimized, pre-built operators for all popular cloud and on-premises relational databases, Airflow makes it easy to design complex SQL-based data pipelines. Data analysts and analytic engineers can also take advantage of popular open-source Airflow add-ons, like Astronomer Cosmos, which allows them to create Airflow DAGs based on dbt Core projects.

• Airflow and Data Scientists: Python is the lingua franca of data science, and Airflow is a Python-based tool for writing, scheduling, and monitoring data pipelines and other workflows. Today, thousands of data scientists depend on Airflow to acquire, condition, and prepare the datasets they use to train their models, as well as to deploy these models to production.

• Airflow and Data Team Leads: Data team leaders rely on Airflow to support the work of the data practitioners they manage, recognizing that Airflow’s ability to reliably and efficiently manage data workflows enables their teams to be productive and effective. Data team leads value Airflow’s Python-based foundation for writing, maintaining, and managing data pipelines as code.

• Airflow and Data Platform Architects: Airflow-powered workflow management underpins the data layers that data platform architects design to knit together modern, cloud-native data platforms. Data platform architects trust in Airflow to automate the movement and processing of data through and across diverse systems, managing complex data flows and providing flexible scheduling, monitoring, and alerting.

Apache Airflow’s architecture is modular and composed of several key components that work together to manage workflow orchestration. These components include:

• Scheduler: The Scheduler is responsible for determining the execution order of tasks. It orchestrates task dependencies and ensures that tasks are run according to the defined schedule. The DAG Processor is part of the scheduler by default and it is responsible for parsing and processing the DAG files to update task information and schedules.

• Workers: Workers execute the tasks defined in the DAGs. Airflow supports different executors to distribute tasks across workers, such as the CeleryExecutor for distributed environments or the KubernetesExecutor for containerized workflows.

• Metadata Database: All DAGs, task statuses, and other workflow metadata are stored in a centralized database. The database serves as the source of truth for task states and schedules, and it's essential for tracking the progress of workflows over time.

• Web Server (UI): Airflow's web server provides an intuitive, browser-based user interface that allows users to monitor and manage workflows visually. This includes seeing DAG runs, task statuses, logs, and more.

• Executor: The executor is a property of the scheduler component that determines where tasks are run. Different executors (e.g., Local, Celery, Kubernetes) determine how tasks are distributed and managed across worker nodes.

• Reliability: High Availability (HA) schedulers in Airflow ensure that the system remains operational even in the event of a failure. By deploying multiple scheduler instances, organizations can achieve redundancy, minimizing downtime.

• Modularity: The independent components of the Airflow architecture allow it to scale horizontally and efficiently handle massive workloads when running on Kubernetes.

• Extensibility: Users can extend the core functionality with custom operators and plugins.

• Complex Scaling: In large-scale environments, managing and scaling Airflow's components (particularly the Scheduler and Workers) can become complex.

• High Maintenance: Configuring and maintaining Airflow’s containerized setup, especially in production, requires significant operational effort.

For beginners, Airflow requires familiarity with Python and basic concepts of workflow orchestration. For more advanced use cases, you’ll need knowledge about scaling Airflow, data-driven scheduling capabilities, as well as Airflow features allowing dynamically adapting pipelines.

However, with a strong community and extensive documentation, users can quickly gain proficiency and leverage Airflow's flexibility for various workflows. The Astronomer Academy, Astronomer webinars and the Astronomer documentation provide free Airflow courses, demos and guides taught by Astronomer’s Airflow experts.

Apache Airflow is highly scalable and can handle large volumes of data and tasks. In a production setup such as Astro, Airflow can distribute tasks across multiple worker nodes, making it suitable for enterprise-level workflows.

Airflow runs on Kubernetes clusters on Astro, enabling dynamic scaling based on workload demands. Its ability to parallelize tasks and efficiently manage dependencies ensures that even complex, high-volume pipelines can be managed with ease, making it a robust solution for large-scale data engineering and machine learning workflows.

Review the documentation to learn about Airflow scaling best practices.

Best practices for designing and maintaining Airflow DAGs include:

• Keep Tasks Atomic: Ideally each task accomplishes one specific action. DAGs designed with atomic tasks give you fine-grained observability into events in your data pipeline and the ability to rerun from the point of failure.

• Design idempotent DAGs and Tasks: When backfilling DAGs for earlier dates it is crucial that the same input for individual tasks and DAGs produce the same output in order to benefit from reliable, reproducible orchestration

• Modularize your code: Airflow is built on Python code and allows you to modularize commonly used actions in Python functions to be reused in several tasks across DAGs. As in all software engineering, it is a good practice to try not to repeat yourself, if you write the same code twice - consider turning it into a reusable module or even to create your own custom Airflow operator.

• Implement Automated Testing: Write unit tests for custom functions and operators, as well as integration and validation tests for your DAGs. Leverage development environments for automatic testing before pushing your code to production.

• Handle Failures: Implement automatic retries and alerts for task failures.

• Use Version Control: Like all code, DAGs and supporting code like SQL scripts should be stored in version control and be governed by a CI/CD process.

Review the documentation to learn about Airflow DAG best practices.

Airflow handles errors through built-in mechanisms like retries and alerts. For any failed task, Airflow can automatically retry based on the number of retries and delay between them, which are configurable. You can also set execution timeouts to prevent tasks from running indefinitely. If a task fails even after retries, Airflow can trigger a callback function, commonly sending email or Slack alerts to notify relevant teams. Additionally, the Airflow UI exposes task logs, making it easier to diagnose and fix issues. These features help maintain workflow stability and reliability.

Astro, powered by Astronomer.io, is a fully managed platform built on top of Apache Airflow, offering a more streamlined architecture for enterprises as depicted in the diagram below.

Key differences between the architecture of Astro over Apache Airflow include:

• Astro Runtime: Handles all infrastructure management automatically, including scaling, so users don’t have to manually configure the Scheduler, Workers, or Executors.

• Dynamic Scaling: Astro adjusts resource allocation dynamically based on workload needs, ensuring tasks run efficiently without over-provisioning resources.

• Multi-Environment Support: Astro supports multiple isolated environments (e.g., dev, staging, production) within a single platform with cross-environment observability, unlike self-hosted Airflow, which typically operates in siloed environments.

• Enhanced Observability: Provides advanced monitoring, real-time alerts, and dashboards to give better insights into DAG performance.

These enhancements make Astro easier to manage, highly scalable, and ideal for enterprise-level workflows. Learn more about Astro’s architecture and how it helps you manage Airflow on Astro.

While Apache Airflow is powerful and flexible, it requires significant operational overhead to manage and scale. Astro offers several advantages:

• Simplified Management: Astro automates the configuration, scaling, and maintenance of Airflow components.

• Enterprise-Ready: Astro includes advanced security features like role-based access control (RBAC) and audit logging.

• Dynamic Scaling: Astro automatically scales resources based on workload demand, optimizing costs and performance.

• Multi-Environment Support: Astro allows businesses to easily manage separate environments for development, staging, and production within the same platform.

Astro's architecture provides dynamic scaling, where the platform automatically allocates resources based on the needs of each DAG. This ensures that workflows run smoothly, even as their complexity grows, without the need for manual configuration or over-provisioning. In contrast, managing scalability in standard Airflow requires careful configuration of executors, workers, and schedulers, which can become operationally complex as workloads increase.

Businesses that operate at scale, manage multi-cloud environments, or require enhanced security and observability should consider using Astro. With its fully managed infrastructure, automated scaling, and advanced monitoring, Astro is particularly suitable for enterprises running mission-critical workflows that need high availability, compliance, and seamless integrations with cloud services.

For more details on how Astro simplifies Airflow, visit Astronomer.io.

Astro addresses many challenges that come with managing Airflow in production environments:

• Operational Complexity: By automating infrastructure management, Astro eliminates the need for manual configuration and scaling of Airflow components.

• Scaling Issues: Astro's dynamic scaling ensures that resources are allocated efficiently, even as workflows grow in complexity, preventing bottlenecks and improving performance.

• Maintenance Overhead: Astro automates software upgrades and system maintenance, reducing the burden on internal teams and ensuring that users always have access to the latest features and improvements in Airflow.

For more in-depth information on how to leverage Astro for your data orchestration needs, check out the Astro platform.