Beyond Airflow - 10 Workflow Tools You Need to Know

Apache Airflow is a popular open-source platform for scheduling and managing workflows. It provides a unified interface for defining, executing, and monitoring workflows, making it a valuable tool for data engineers and scientists. However, Apache Airflow is not the only option available, and in some cases, other alternatives may be better suited to meet the specific needs of a project. In this blog post, we will explore some alternatives to Apache Airflow that can be used to manage workflows and schedule tasks.

• 1. Prefect
• 2. Luigi
• 3. Apache Nifi
• 4. Argo
• 5. Zeebe
• 6. DAGster
• 7. Conductor
• 8. Kubeflow Pipelines
• 9. Oozie
• 10. Pinball
• Summary

1. Prefect

Prefect is a modern, serverless, and open-source workflow management platform that was built to tackle the complexities of data-driven workflows. It aims to provide a simple, intuitive interface for defining, executing, and monitoring workflows, while also offering a robust set of features for handling complex use cases. Prefect offers a wide range of integrations with other tools, including popular cloud platforms like AWS, GCP, and Azure. Prefect also has a growing community of users and developers, making it an excellent option for projects of all sizes.

Link: https://www.prefect.io/

2. Luigi

Luigi is a Python-based workflow management system that was created by Spotify. It is designed to be a simple and efficient way to build complex pipelines of batch jobs. Luigi offers a simple interface for defining tasks and dependencies, and it can easily be integrated with other tools and services, such as databases and cloud platforms. Luigi is a great option for projects that require a high degree of customization and flexibility.

Link: https://github.com/spotify/luigi

3. Apache Nifi

Apache NiFi is a powerful data integration tool that can be used to manage workflows. NiFi provides a user-friendly interface for designing, executing, and monitoring data flows, making it a great option for projects that require a high degree of control and visibility into the flow of data. NiFi supports a wide range of data sources and destinations, and it can be used to automate many different types of data-driven workflows.

Link: https://nifi.apache.org/

4. Argo

Argo is an open-source workflow management system that was designed specifically for Kubernetes. Argo provides a simple and efficient way to manage and automate complex workflows on a Kubernetes cluster. It offers a range of features for defining, executing, and monitoring workflows, and it can be easily integrated with other tools and services, such as databases and cloud platforms. Argo is a great option for projects that require a high degree of scalability and reliability.

Link: https://argoproj.github.io/

5. Zeebe

Zeebe is an open-source workflow engine for microservices orchestration. It is designed to be highly scalable, providing a reliable and efficient way to manage complex workflows across multiple microservices. Zeebe offers a user-friendly interface for defining and executing workflows, and it provides a range of features for monitoring and debugging workflows. Zeebe is a great option for projects that require a high degree of coordination and control over microservices.

Link: https://zeebe.io/

6. DAGster

DAGster is an open-source data orchestration platform that was created to manage complex data pipelines. It offers a modular architecture that allows users to easily compose complex workflows, making it a great option for teams that need to build sophisticated data-driven applications. DAGster also offers powerful features for testing, debugging, and monitoring pipelines.

Link: https://dagster.io/

7. Conductor

Conductor is a workflow orchestration engine that is designed to manage complex and long-running workflows. It offers a user-friendly interface for designing and executing workflows, and it provides a range of features for scaling workflows and handling errors. Conductor is also highly customizable and can be easily integrated with other tools and services.

Link: https://netflix.github.io/conductor/

8. Kubeflow Pipelines

Kubeflow Pipelines is a platform for building and deploying machine learning pipelines on Kubernetes. It offers a wide range of features for defining and executing machine learning workflows, making it a great option for data scientists and engineers. Kubeflow Pipelines also provides a user-friendly interface for designing and monitoring workflows, and it can be easily integrated with other Kubeflow components.

Link: https://www.kubeflow.org/docs/components/pipelines/

9. Oozie

Oozie is a workflow scheduler system for Hadoop that is designed to manage batch jobs and data processing workflows. It offers a powerful set of features for defining and executing workflows, and it can be easily integrated with other Hadoop components. Oozie also provides a user-friendly interface for managing workflows and monitoring job status.

Link: https://oozie.apache.org/

10. Pinball

Pinball is a workflow management system that was created by Pinterest. It offers a user-friendly interface for defining and executing workflows, and it provides a range of features for handling dependencies and managing job execution. Pinball also offers a powerful set of APIs and can be easily integrated with other tools and services.

Link: https://github.com/pinterest/pinball

Summary

Apache Airflow is a powerful tool for managing workflows, but it is not the only option available. Each of the alternatives discussed above offers its own unique set of features and advantages, making them worth considering for projects with specific requirements.

NOTE: some projects may benefit from using a combination of workflow management systems. For example, a project might use Apache Airflow to manage some workflows while using Zeebe for managing microservices orchestration. This approach can help to take advantage of the unique features and advantages of different systems while minimizing their limitations.

Any comments or suggestions? Let me know.

Credits: Heading photo from Unsplash by Sigmund

@article{Saf2023Beyond,
    author  = {Krystian Safjan},
    title   = {Beyond Airflow - 10 Workflow Tools You Need to Know},
    journal = {Krystian's Safjan Blog},
    year    = {2023},
}