Airflow vs OTRS Community Edition Comparison | StackShare

Overview

Airflow

Stacks1.7K

Followers2.8K

Votes128

OTRS Community Edition

Stacks12

Followers15

Votes0

GitHub Stars858

Forks451

Airflow vs OTRS Community Edition: What are the differences?

What is 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.

What is OTRS Community Edition? A web-based ticketing systems used for Customer Service, Help Desk, IT Service Management. It is one of the most flexible web-based ticketing systems used for Customer Service, Help Desk, IT Service Management. With a fast implementation and easy customization to your needs it helps you reducing costs and increasing the efficiency and transparency of your business communication.

Airflow belongs to "Workflow Manager" category of the tech stack, while OTRS Community Edition can be primarily classified under "Issue Tracking".

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, OTRS Community Edition provides the following key features:

Support for MIME emails with attachments
Automatic conversion of HTML into plain text messages (increased security for sensitive content and enables faster searching)
Incoming mail can be filtered and pre-processed with complex rules, e.g. for spam messages or Queue distribution

Airflow and OTRS Community Edition are both open source tools. Airflow with 16.4K GitHub stars and 6.3K forks on GitHub appears to be more popular than OTRS Community Edition with 670 GitHub stars and 416 GitHub forks.

Advice on Airflow, OTRS Community Edition

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	OTRS Community Edition
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 one of the most flexible web-based ticketing systems used for Customer Service, Help Desk, IT Service Management. With a fast implementation and easy customization to your needs it helps you reducing costs and increasing the efficiency and transparency of your business communication.
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.	Support for MIME emails with attachments; Automatic conversion of HTML into plain text messages (increased security for sensitive content and enables faster searching); Incoming mail can be filtered and pre-processed with complex rules, e.g. for spam messages or Queue distribution; Support for PGP and S/MIME standards for key/certificate management and email processing; Automatic responses, configurable for every queue
Statistics
GitHub Stars -	GitHub Stars 858
GitHub Forks -	GitHub Forks 451
Stacks 1.7K	Stacks 12
Followers 2.8K	Followers 15
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

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