Apache Airflow has become the most popular platform for data engineering, pipeline management, and workflow automation since its genesis as an internal data management tool at Airbnb a decade ago. The powerful open source tool enables organizations to streamline their data engineering processes, ensuring reliability, scalability, and maintainability in managing diverse data pipelines across disparate systems and environments.
In this Apache Airflow review, we’ll explore the fundamental concepts behind the tool—its architecture, components, core functionality, and the key principles underpinning its design philosophy for data workflow orchestration and execution—and give our expert opinion on how building data pipelines with Apache Airflow compares to its leading competitors.
Table of Contents
What is Apache Airflow?
Apache Airflow is an open source platform designed to simplify and streamline the process of orchestrating and managing complex data workflows. Originally developed to address Airbnb’s growing data infrastructure needs, Airflow became an open source project under the auspices of the Apache Software Foundation, which maintains it. The platform has become a go-to solution for data engineers, data scientists, and DevOps professionals to orchestrate and automate their data engineering pipelines.
Airflow’s framework for defining, scheduling, and executing workflows revolves around the concept of the directed acyclic graphs (DAG), a mechanism that allows for the clear, concise expression of dependencies and relationships between different tasks. DAGs enable much of Apache Airflow’s flexibility and extensibility, extending a rich set of features that enable users to create dynamic and scalable workflows tailored to their specific requirements.
Python for the Win
Because Python is the programming lingua franca of the data science world, Apache Airflow’s Python-based core contributes to its market dominance. The platform’s configuration and plug-in architecture, both written in Python, allow for easy integrations with a wide range of systems and services—databases, cloud platforms, and third-party tools, for example. This flexibility empowers users to build workflows that seamlessly integrate with their existing infrastructure and adapt to evolving business needs.
Airflow’s Approach to DAGs
The science behind DAGs is heavily steeped in graph theory, mathematics, and computer science, though the premise is fairly straightforward. A DAG is a graph that is both directed and acyclic—that is, actions and processes only move in one direction and cannot go backward.
For example, a group of tasks to be executed sequentially can each be represented as points connected by lines. The lines represent the order of execution precedence. Loops are not allowed, so you can’t go back to an already-executed task. Essentially, a DAG is a roadmap for getting tasks done without going back and redoing tasks once they are complete.
DAGs are the underlying mechanisms that power Apache Airflow’s robust scheduler, ensuring the reliable execution of tasks according to predefined dependencies and schedules. Airflow’s scheduler maintains a DAG of tasks, automatically determining the order of execution based on task dependencies and external triggers. This enables users to orchestrate complex workflows involving multiple tasks, parallel execution, and conditional branching with ease. With Airflow’s DAGs, users can represent the dependencies and relationships between tasks in a clear and intuitive manner.
Apache Airflow Pricing
As an open source project, Apache Airflow is free to download, use, and modify, making it an attractive option for organizations seeking a cost-effective solution for workflow orchestration and automation. But there’s an old saying that open source software is “free like kittens, not free like beer,” which means that while the software itself does not cost anything, the associated expenses and resource and infrastructure considerations to implement Apache Airflow can add up.
Organizations looking to host and manage their own Airflow implementations should budget for expenses associated with provisioning and maintaining the infrastructure required to run Airflow, including compute resources, storage, and networking. And depending on the scale and complexity of the workflows being orchestrated, these costs can vary significantly—especially for organizations operating in cloud environments where resource usage is billed on a pay-as-you-go basis.
Companies may also choose to invest in training and support services to help onboard their teams and ensure smooth adoption and operation of Apache Airflow. While there are ample resources available online—including documentation, tutorials, and community forums—organizations may opt to invest in formal training programs or consulting services to accelerate their learning curve and address specific use cases or challenges. These costs should be factored into the overall budget for deploying Apache Airflow within an organization.
Other costs may include integration-related expenditures—depending on the complexity of these integrations and the level of customization required, firms may need to allocate resources for development, testing, and maintenance to ensure seamless interoperability between Airflow and other components of their data infrastructure.
Apache Airflow Key Features and Benefits
Airflow provides a rich set of monitoring and logging capabilities, allowing users to track the progress of workflows, troubleshoot errors, and gain insights into performance metrics in real-time. Unlike traditional job schedulers, Airflow’s DAGs provide a flexible and expressive way to model complex workflows with multiple branches, parallel execution, and conditional logic.
Airflow’s extensible architecture enables seamless integrations with a wide range of systems and services. Its Python-based configuration and plugin system allow users to extend Airflow’s functionality to suit their specific requirements.
Whether it’s integrating with cloud platforms like AWS, Google Cloud, or Azure, or connecting to databases, messaging systems, or custom APIs, this extensibility makes it a versatile choice for firms looking to orchestrate workflows across heterogeneous environments and complex data ecosystems.
Airflow allows users to create dynamic workflows that adapt to changing conditions and data inputs. With its support for template variables, dynamic task generation, and parameterized execution, Airflow enables users to build workflows that are flexible and resilient to variations in data volume, frequency, and schema.
This feature is particularly valuable for firms operating in dynamic and fast-paced environments like e-commerce, advertising, and fintech, where data sources and processing requirements may evolve rapidly over time.
Built-in Monitoring and Alerting
Apache Airflow provides robust monitoring and alerting capabilities out of the box, allowing users to track the progress of workflows, monitor task execution status, and receive notifications about failures or anomalies. With its built-in web interface and integrations with popular monitoring tools like Prometheus and Grafana, Airflow makes it easy for firms to gain visibility into their data pipelines and ensure reliability and performance.
Apache Airflow Pros
Apache Airflow offers numerous advantages that make it a popular choice for workflow orchestration and automation:
- Flexible, Expressive Workflow Definitions—DAG-based workflow definitions provide a flexible and expressive way to model complex dependencies and relationships between tasks, enabling dynamic and scalable workflows tailored to specific requirements, with support for parallel execution, conditional branching, and error handling.
- Extensible Architecture—Airflow’s Python-based configuration and plug-in system make it easy to extend and customize its functionality to suit diverse use cases and integration needs.
- Rich Featureset—Apache Airflow offers a comprehensive set of features for workflow orchestration and automation, including task scheduling, monitoring, logging, alerting, and more; its integration with popular monitoring tools allow for advanced monitoring and analytics capabilities.
- User-Friendly Interface—Airflow’s user-friendly built-in web interface lets you manage workflows, monitor task execution status, and troubleshoot errors easily.
- Active Community and Ecosystem—As an open source solution, Airflow is supported by a vibrant, active community of developers and contributors that enhance and extend its capabilities on an ongoing basis; this collaborative ecosystem ensures it is continuously updated with new features and integrations that address emerging use cases and industry trends.
- Scalability and Reliability—Airflow is designed to scale horizontally, allowing users to handle increasing workloads and data volumes with ease; its distributed architecture and support for task parallelization enable efficient resource utilization and high throughput, even in large-scale production environments.
Apache Airflow Cons
While Apache Airflow offers numerous benefits, data pipeline architects should keep in mind the following potential drawbacks or limitations:
- Steep Learning Curve—Apache Airflow has a steep learning curve, especially for users who are new to workflow orchestration and automation concepts; understanding how to define and manage DAG workflows, configuring and scheduling tasks, and troubleshooting errors require time and effort for novice or intermediate data architects and engineers.
- Resource Intensive—Airflow can be resource-intensive, especially in environments with large-scale workflows or high-frequency task execution; managing and scaling Airflow clusters to handle increasing workloads and data volumes requires careful optimization of resources (compute, memory, storage) to ensure efficient operation and performance.
- Complexity of Deployment—Deploying Airflow in production environments can be complex, particularly in distributed or cloud-based architectures; configuring and managing clusters, setting up dependencies, securing access controls, and ensuring high availability and fault tolerance can present challenges for organizations with limited expertise or resources in infrastructure management.
- Limited Native Support for Stateful Workflows—While Airflow excels at orchestrating stateless batch processing workflows, it may not be the best fit for managing stateful or long-running workflows, such as stream processing or real-time data processing; while it’s possible to implement workarounds or integrate with external systems, this may add complexity and overhead.
- Lack of Support for Advanced Features—Despite its large open source community and ecosystem, support for advanced features or niche use cases may be limited, especially compared to commercial workflow orchestration platforms; customizing or extending Airflow to address specific requirements may require significant development efforts or third-party development support.
Apache Airflow Alternatives
Apache Airflow is the leading solution for workflow orchestration and automation, but it’s not the only one. Several alternatives provide distinct features and capabilities that may better suit specific data engineering use cases or organizational requirements.
Keboola is a cloud-based data integration and analytics platform that allows organizations to streamline and automate their data operations. Unlike Apache Airflow, which focuses primarily on workflow orchestration and automation, Keboola offers a more comprehensive suite of features, including data extraction, transformation, and loading (ETL); data storage; analytics; and visualization.
Compared to Airflow, Keboola’s all-in-one platform offers a more integrated and user-friendly solution for end-to-end data operations, making it well-suited for organizations seeking a comprehensive data management solution without the need for extensive coding or infrastructure management.
Originally developed by Spotify’s engineering team, Luigi is an open-source Python-based workflow management tool designed to facilitate the orchestration of complex data pipelines. Like Airflow, Luigi allows users to define workflows as DAGs, with tasks represented as nodes and dependencies between tasks represented as edges.
But Luigi tends to focus more on simplicity and ease of use, with a more lightweight and minimalist approach compared to Airflow’s extensive featureset. Additionally, Luigi offers built-in support for features such as dependency resolution, task prioritization, and workflow visualization, making it a popular choice for data engineers looking for a lightweight, straightforward solution for managing data pipelines without the need for complex configurations or setups.
Dagster is a data orchestrator and workflow engine designed to make data engineering easy, scalable, and robust. Unlike Apache Airflow, which focuses primarily on workflow orchestration and automation, Dagster takes a more holistic approach by providing a unified programming model for defining data pipelines, managing dependencies, and orchestrating execution.
With Dagster, users can define pipelines using a Python-based API, with support for strong typing, dependency injection, and declarative configuration. Additionally, Dagster offers built-in support for data lineage, versioning, testing, and debugging, making it well-suited for organizations seeking a more comprehensive solution for managing end-to-end data workflows and operations.
While both Dagster and Apache Airflow enable workflow orchestration and automation, Dagster’s emphasis on data engineering best practices and developer-friendly features sets it apart as a powerful tool for building reliable and scalable data pipelines.
How We Evaluated Apache Airflow
In evaluating Apache Airflow, we employed a comprehensive approach that encompassed several key factors to assess its performance, usability, and suitability for various use cases.
Flexibility and Expressiveness
Apache Airflow’s DAG-based workflow definitions allow for extensive flexibility and expressiveness in representing complex dependencies and relationships between tasks. This in turn enables the creation of dynamic, scalable pipelines that can adapt to changing data processing needs.
Scalability and Performance
Apache Airflow’s scalability and performance are crucial for organizations managing large-scale data processing workloads—specifically, its innate ability to handle increasing volumes of data and concurrent task execution while maintaining optimal performance and resource utilization. Scalability considerations include support for distributed task execution, horizontal scaling of worker nodes, and efficient resource management.
Reliability and Fault Tolerance
Apache Airflow’s reliability and fault tolerance ensures that workflows are executed correctly and consistently even in the presence of failures or errors. Features like task retries, advanced error handling, and monitoring/alerting components contribute to Airflow’s reliability and fault tolerance, allowing organizations to maintain data pipeline integrity and ensure business continuity.
Community and Ecosystem Support
Apache Airflow’s thriving community and ecosystem continue to provide valuable resources, support, and opportunities for collaboration, resulting in the open source workflow orchestration platform’s enhanced overall usability, widespread adoption, and longevity.
Frequently Asked Questions (FAQs)
How does Apache Airflow differ from traditional job schedulers?
Unlike traditional job schedulers, Apache Airflow represents workflows as DAGs, allowing for more flexible and expressive workflow definitions. Airflow also provides a rich set of features for monitoring, logging, and alerting, making it well-suited for managing modern data infrastructure.
Can Apache Airflow be used for real-time data processing?
While Apache Airflow is primarily designed for orchestrating batch processing workflows, the platform can also be used for near-real-time data processing. Airflow supports task execution at intervals as short as one minute, making it suitable for scenarios requiring low-latency data processing.
Is Apache Airflow suitable for large-scale deployments?
Apache Airflow is designed to scale horizontally, allowing users to deploy and manage large-scale workflow orchestration systems. Airflow supports distributed task execution across multiple worker nodes, providing scalability and fault tolerance for handling high volumes of data and concurrent workflows.
Can Apache Airflow integrate with other systems and services?
Apache Airflow supports integration with a wide range of systems and services through its extensible architecture and plugin system. Airflow can integrate with databases, cloud platforms, messaging systems, monitoring tools, and more, allowing users to build end-to-end data pipelines that span multiple environments and technologies.
What kind of support and resources are available for Apache Airflow users?
Apache Airflow boasts a highly active community of users, contributors, and developers who provide support, documentation, tutorials, and resources for users of all levels. In addition to the official Apache Airflow documentation, users can find tutorials, blogs, forums, and meetups dedicated to Airflow-related topics, making it easy to learn, troubleshoot, and collaborate with others in the community.
Bottom Line: Apache Airflow, the Best Tool for Data Pipelines
Apache Airflow is a tried-and-true platform for organizations looking to streamline and optimize their data pipelines. With battle-tested attributes like its flexible, expressive workflow definitions, extensible architecture, rich set of features, and active community support, the platform will continue to hold court as the go-to solution for orchestrating and automating complex data workflows.
If you’re interested in data pipelines, read our comprehensive guide to their design to learn more about the challenges they pose and what goes into their creation and design.