Airflow vs Kestra: Choosing the best workflow tool
Choosing the right orchestrator to manage your workflow can be challenging, as you want a tool that best works for your business. Apache Airflow and Kestra are two powerful orchestrators for managing workflows and data pipelines. You are probably already familiar with Airflow, as it is one of the most used tools by data engineers. In this article, we will compare both tools, exploring their similarities and differences, and highlighting key features to consider when choosing a workflow tool.
What is Airflow
Apache Airflow is one of the oldest workflow management platforms, making it a popular choice amongst data engineers. Created by Airbnb, this platform enables data engineers to develop, schedule and monitor workflows and pipelines programmatically. It allows users to define workflows as Directed Acyclic Graphs (DAGs) and provides a rich user interface which enables users easily visualize and monitor their workflows.
What is Kestra
Kestra is a new-generation workflow automation tool that allows data engineers and users to build reliable workflows. It comes with a declarative YAML interface, which makes it easier for everyone to be involved in the process of designing and managing workflows, even non-coders. It is easy to use and provides an extensive number of plugins.
Airflow vs Kestra
Both platforms have unique capabilities, as well as common features they share. Here is a table comparing them:
Ease of use and set-up
Python script (DAGS)
YAML or JSON files(flows)
Large community support
Extensible operators and plugins
Extensible operators and plugins
Time-based and event-driven
Both platforms provide a rich web user interface for managing and monitoring workflows, providing a centralized view of the workflows via dashboards. However, unlike Airflow where the user interface is used merely for monitoring and managing workflows, Kestra allows you to both build and execute your tasks directly from its UI on the flow editor, while still monitoring and managing the state of your task.
Ease of usage and set-up:
Kestra is easier to set up and use than Airflow. Its declarative YAML interface makes it accessible to users with varying skill levels. Its UI comes with a flow editor that provides an autocompletion feature, an extensive list of blueprints to guide users on how to make flows, and documentation directly in the UI.
Airflow uses DAGs and Python scripts to represent the dependencies between workflows, while Kestra uses a concept called "flows" to represent the dependencies between tasks.
Community and Ecosystem:
Both platforms are open-source tools with an active community on Github and Slack. Being the oldest workflow tool, Airflow has a larger open-source community than Kestra, a relatively new tool.
Parallel Task Execution:
Both platforms support the parallel execution of tasks, meaning that with either of the platforms, you can execute two independent tasks concurrently. This improves the overall efficiency and speed of your workflow.
Extensibility and Customization:
Both platforms provide plugins, operators, and hooks that enable users to build custom solutions, integrate with various technologies, and customize workflows to fit their specific needs.
Scheduling and Triggering:
As with most workflow orchestrators, Kestra and Airflow support the scheduling of workflows, allowing you to specify when and how frequently a workflow should be executed. Airflow provides options for time-based scheduling and cron expressions, while Kestra utilizes both time-based and event-based triggering to initiate the execution of workflows.
Choosing the Right Platform for Your Workflow
Choosing the right platforms largely depends on what your pipeline or work requires, and what platform you are comfortable with. However, here is a list of factors you can consider when deciding what platform is best for you:
Community: Community support is an important factor to consider when choosing a workflow automation platform. Airflow has a large and active community, with over 30,000 contributors on GitHub compared to Kestra. Also, being the oldest workflow platform, data engineers have been able to provide many resources for Airflow online, including tutorials, documentation, and troubleshooting guides.
Ease to Use: In deciding on the best platform, usage is an important criterion to consider. Kestra's declarative YAML interface makes it a favourable tool compared to Airflow in this case. YAML configurations are in a human-readable format that is easy to understand, compared to Airflows DAGs. With Kestra's declarative YAML interface, everyone can get involved in workflow orchestration including non-coders.
Version Control: As your project progresses and more layers are added, managing changes to complex workflows becomes very important, especially when multiple data engineers are working on the same project. Kestra offers a better solution in this regard, its YAML-based system provides control over individual tasks and components, making it easier to version and manage changes.
Workflow Complexity: The best workflow fit for your needs depends on the complexity of your workflow. Airflow is designed to handle large-scale and complex workflows with extensive dependencies between tasks. However, it can be difficult to execute lightweight workflows with Airflow. Kestra, on the other hand, is designed to be lightweight and scalable, making it efficient for handling both simple and complex workflows.
Data Processing: The data processing method for automating your workflow is another factor to consider when making a choice. Both platforms are well-suitable for workflows that require batch processing or recurring tasks that can be scheduled at specific time intervals, as they provide time-based scheduling to execute workflows. However, for workflows that require processing real-time data or requires immediate action based on events, Kestra is a suitable tool, as it is the best fit for event-based automation.
Ultimately, the best tool for your orchestration depends on your business need. Choosing the right tool for your workflow significantly impacts your project/workflow's performance. Apache Airflow and Kestra provide constructive solutions in helping you achieve effective workflow automation, each with its unique features and use cases.