Skip to content

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.

Want to know more tips about coding and other things, please visit: https://github.com/seanslma/maki