Introduction
Apache Airflow is an open-source tool designed to orchestrate complex computational workflows and data processing pipelines. Developed by Airbnb in 2014 and later incubated by the Apache Software Foundation, Airflow enables data engineers to author, schedule, and monitor workflows programmatically.
Key Features
- Dynamic Scheduling: Airflow’s dynamic task scheduling capabilities allow you to define workflows as Directed Acyclic Graphs (DAGs). Each DAG defines a sequence of tasks and their dependencies.
- Scalable: It can scale to handle multiple operations concurrently, making it suitable for enterprises with large data sets and complex workflows.
- Extensible: Offers a variety of Operators which allow for the execution of different tasks, including Bash, Python, SQL, and more. You can also define your custom operators.
- Rich User Interface: Provides a detailed dashboard that allows for easy monitoring and managing of workflows, visualising dependencies, and tracking logs.
- Community-Driven Integrations: Extensive support for integration with major platforms like AWS, Azure, Google Cloud, and many other services and databases due to its robust community.
Who Develops the Product
Apache Airflow is maintained by the Apache Software Foundation, which ensures its stability and ongoing development. The project benefits from a large community of contributors who continuously enhance its features and maintain its operations.
Product Maturity
Apache Airflow is considered a mature product in the modern data stack, widely adopted by companies of various sizes. While it is constantly evolving with regular updates to fix bugs and add new features, it also maintains a stable core that many enterprises rely on for critical operations.
Usage Examples
Batch Data Processing
Automate the nightly batch processes to aggregate data from various sources into a data warehouse using Airflow DAGs that handle dependencies and retries systematically.
Real-Time Data Handling
Use Airflow to manage workflows that involve real-time data processing by triggering tasks that respond to external events.
Integration Capabilities
Airflow boasts extensive API support and built-in connectors that make it adaptable to various environments, facilitating seamless integration with databases, messaging queues, and other data processing services across the data engineering landscape.
Target Market
Apache Airflow is particularly popular among mid to large-sized enterprises that require robust workflow management. Its scalability and flexibility make it a favourite in industries like e-commerce, financial services, and technology.
Pricing
Apache Airflow is an open-source project under the Apache Foundation, and thus, it is free to use. However, operational costs can arise when deployed at scale — typically in cloud environments where compute resources are priced. Many companies leverage managed services like Astronomer, which offers Airflow as a service and charges based on computing resources consumed and the level of support required.
Reception
Data Engineers
While data engineers generally appreciate Airflow for its robustness and flexibility, some critique it for its steep learning curve and occasionally cumbersome UI for managing complex dependencies. That being said, it is exceedingly popular on the data engineering subreddit.
Executives
Executives favour Apache Airflow due to its open-source nature and the strong community support, which reduces the risk associated with proprietary solutions. The tool’s ability to integrate with a wide array of systems often aligns well with strategic goals of reducing vendor lock-in and fostering agile IT environments.