Which orchestration tool is better: Airflow, Prefect, Argo Workflows, or Temporal?
Nowadays there are many tools for task orchestration. Some popular ones include Airflow, Prefect, Argo Workflows, and Temporal. Now the question is which tool should I use in my team?
Here I will briefly list the features of the four task orchestration tools. Hopefully this will help you decide which tool is best for your task scheduling.
Airflow
Airflow is a popular task scheduling tool. The data workflows in Airflow are defined using Python.
It has a large user base but also has some limitations as it was created earlier than other orchestration tools:
- DAGs are not parameterized - you can’t pass parameters into your workflows
- DAGs are static - it can’t automatically create new steps at runtime as needed
- We have to package the entire workflow into one container
Prefect
Prefect was created to overcome some of the limitations in Airflow, with a strong emphasis on ease of use and deployment, especially for complex DAGs.
Some features of Prefect:
- Workflows are defined in Python, parameterized and dynamic
- Can run each step in a container, but need to register docker with workflows in Prefect
- Uses state management abstractions that allow for easy retries and failure handling within data workflows
- Has built-in integrations with popular data engineering tools and platforms, such as Dask, DBT, and various cloud services
Argo Workflows
Argo Workflows is a container-native workflow engine for orchestrating jobs on Kubernetes. It naturally addresses the deployment issue in Airflow and Prefect.
- Workflows are defined in YAML
- Every step in an workflow is run in its own container
- Relies on Kubernetes for state management
- Can only run on Kubernetes clusters
Temporal
While Airflow, Prefect and Argo Workflows focus primarily on data workflow orchestration, Temporal is a more general-purpose workflow tool.
- Workflows are defined using the language for the tasks
- Provides robust features for state management, retries, and long-running processes
- Requires more investment in learning - has a steep learning curve
Summary
As we can see, each tool has its own use cases. We need to select the tool that is most suitable for our work.
- If our tasks are about data processing, we perhaps should consider Airflow, Prefect or Argo Workflows.
- If we already have our tasks running on Kubernetes cluster, for easy of deployment we might choose Argo Workflows.
- If our tasks are more general and require high robustness and reliability, we had better to use Temporal.
- If our workflows are complex, a tool using a programming language instead of yaml files might be more suitable.
- If our workflows are simple and we do not want to invest too much time in learning, a tool with ease of use might be the best choice.