Airflow vs Freeter Comparison | StackShare

Overview

Airflow

Stacks1.7K

Followers2.8K

Votes128

Freeter

Stacks6

Followers7

Votes0

Airflow vs Freeter: What are the differences?

Airflow: A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb. 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; Freeter: Gather all the things you need for work in one place and have quick access to them. It is a productivity app that allows you to gather everything you need to work in one place and access them quickly and easily. It lets you work on as many projects as you want. Use the project dropdown to switch between them easily.

Airflow and Freeter can be categorized as "Workflow Manager" tools.

Some of the features offered by Airflow are:

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.

On the other hand, Freeter provides the following key features:

Structure your workflows
Quickly organize to-do lists
Access web apps and pages

Airflow is an open source tool with 13.8K GitHub stars and 5.11K GitHub forks. Here's a link to Airflow's open source repository on GitHub.

Advice on Airflow, Freeter

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.

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Comments

Detailed Comparison

Airflow	Freeter
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 productivity app that allows you to gather everything you need to work in one place and access them quickly and easily. It lets you work on as many projects as you want. Use the project dropdown to switch between them easily.
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.	Structure your workflows;Quickly organize to-do lists;Access web apps and pages;Work on multiple projects;Gives you quick access to all the things you need to work on a project
Statistics
Stacks 1.7K	Stacks 6
Followers 2.8K	Followers 7
Votes 128	Votes 0
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	No community feedback yet
Integrations
No integrations available	Chargebee Slack PayPal Chargify Asana Mailchimp HipChat Ghost Basecamp Jira

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