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Airflow vs n8n: What are the differences?
Introduction
Airflow and n8n are both workflow automation tools that can help developers and organizations to manage and schedule their workflows. However, there are several key differences between the two tools.
Deployment Approach: While both Airflow and n8n are open-source tools, they have different approaches to deployment. Airflow follows the traditional client-server architecture where the users need to setup and manage the Airflow server. On the other hand, n8n is a self-hosted tool that can be easily deployed using Docker or directly on a server. This makes n8n more suitable for small-scale deployments or personal workflows, while Airflow is better suited for large-scale enterprise deployments.
Interface and User Experience: Airflow provides a rich web-based UI that allows users to visually create and manage workflows using its drag-and-drop interface. It offers a wide range of functionalities and enables users to easily configure tasks, dependencies, and scheduling. In contrast, n8n provides a more simplistic and lightweight interface. Although it lacks some of the advanced features of Airflow, n8n offers an intuitive and easy-to-use interface that makes it ideal for users who prefer simplicity and quick setup.
Supported Integrations and Connectors: Airflow comes with a large number of built-in integrations and connectors that allow users to easily interact with various external systems and services. It has support for various databases, cloud platforms, message queues, and more. On the other hand, n8n provides a wide range of integrations and connectors as well, but it may have a smaller set compared to Airflow. The availability of integrations and connectors is an important factor to consider depending on the specific needs and requirements of your workflows.
Complexity and Learning Curve: Airflow is known for its robustness and scalability, but it also comes with a steeper learning curve. It requires a good understanding of concepts like Directed Acyclic Graphs (DAGs) and the Airflow scheduler, which may take some time for new users to grasp. On the other hand, n8n offers a simpler and more user-friendly approach, making it easier for beginners to get started quickly. It provides a visual workflow editor that allows users to easily connect nodes and define workflows without needing to write code.
Community and Support: Both Airflow and n8n have active communities and offer support through their respective forums and online communities. Airflow, being an Apache project, has a larger community and a wealth of online resources available. It has been around for a longer time and is backed by Apache Software Foundation. n8n, on the other hand, is a relatively newer project but has gained popularity due to its simplicity and ease of use. The community support for n8n is growing rapidly, and it has an active development team that constantly adds new features and improvements.
License and Cost: Airflow is released under the Apache License 2.0, which allows users to use, modify, and distribute the software freely. However, setting up and managing the Airflow server infrastructure may involve costs, especially for large-scale deployments. On the other hand, n8n is completely free and open-source, and the self-hosted nature of n8n makes it a cost-effective solution for smaller workflows and personal use.
In summary, Airflow and n8n are both powerful workflow automation tools but differ in their deployment approach, interface, supported integrations, complexity, community support, and licensing. The choice between the two depends on the specific needs and requirements of the workflows and the preferences of the users.
I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.
For a non-streaming approach:
You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.
Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation
Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of n8n
- Free18
- Easy to use9
- Self-hostable9
- Easily extendable9
- Easily exteandable6
- Powerful5
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Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1