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  1. Stackups
  2. Utilities
  3. Task Scheduling
  4. Workflow Manager
  5. Airflow vs n8n

Airflow vs n8n

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
n8n
n8n
Stacks265
Followers280
Votes59
GitHub Stars154.0K
Forks49.1K

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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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Advice on Airflow, n8n

Anonymous
Anonymous

Jan 19, 2020

Needs advice

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.

294k views294k
Comments

Detailed Comparison

Airflow
Airflow
n8n
n8n

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

It is a free node based Workflow Automation Tool. Easily automate tasks accross different services. Synchronise data between different apps/databases.

Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Free / Open Source; Create own nodes to integrate third-party services or in-house tools;Decide who gets access and where your data is stored; Automate daily tasks; React to events; Easily extendable
Statistics
GitHub Stars
-
GitHub Stars
154.0K
GitHub Forks
-
GitHub Forks
49.1K
Stacks
1.7K
Stacks
265
Followers
2.8K
Followers
280
Votes
128
Votes
59
Pros & Cons
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 1
    Logical separation of DAGs is not straight forward
Pros
  • 19
    Free
  • 10
    Easy to use
  • 9
    Self-hostable
  • 9
    Easily extendable
  • 6
    Easily exteandable
Integrations
No integrations available
Disqus
Disqus
GitHub
GitHub
Google Drive
Google Drive
GitLab
GitLab
Segment
Segment
Redis
Redis
HubSpot
HubSpot
Jira
Jira
Mailgun
Mailgun
Dropbox
Dropbox

What are some alternatives to Airflow, n8n?

Zapier

Zapier

Zapier is for busy people who know their time is better spent selling, marketing, or coding. Instead of wasting valuable time coming up with complicated systems - you can use Zapier to automate the web services you and your team are already using on a daily basis.

IFTTT

IFTTT

It helps you connect all of your different apps and devices. You can enable your apps and devices to work together to do specific things they couldn't do otherwise.

GitHub Actions

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Ghost Inspector

Ghost Inspector

It lets you create and manage UI tests that check specific functionality in your website or application. We execute these automated browser tests continuously from the cloud and alert you if anything breaks.

Apache Beam

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Zenaton

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

Luigi

Luigi

It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

Unito

Unito

Build and map powerful workflows across tools to save your team time. No coding required. Create rules to define what information flows between each of your tools, in minutes.

Integromat

Integromat

It is an easy to use, powerful tool with unique features for automating manual processes. Connect your favorite apps, services and devices with each other without having any programming skills.

Shipyard

Shipyard

na

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