Airflow vs OTRS

Overview

Airflow

Stacks1.7K

Followers2.8K

Votes128

OTRS

Stacks22

Followers44

Votes0

GitHub Stars858

Forks451

Airflow vs OTRS: 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? A customizable support desk software that manages workflows and structures communication. It is a modern, flexible ticket and process management system that allows service management professionals in any industry to keep pace with today’s lightning-fast,results-driven business environment.

Airflow and OTRS 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, OTRS provides the following key features:

Help Desk Ticket Creation
Self-Service Tools
Dashboard with Filter Options

Airflow and OTRS are both open source tools. It seems that Airflow with 14.3K GitHub stars and 5.36K forks on GitHub has more adoption than OTRS with 620 GitHub stars and 398 GitHub forks.

Airbnb, Slack, and 9GAG are some of the popular companies that use Airflow, whereas OTRS is used by GameDuell, FranceConnect, and Sicoob Credija. Airflow has a broader approval, being mentioned in 113 company stacks & 193 developers stacks; compared to OTRS, which is listed in 3 company stacks and 3 developer stacks.

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Airflow

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A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb

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Advice on Airflow, OTRS

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
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 modern, flexible ticket and process management system that allows service management professionals in any industry to keep pace with today’s lightning-fast,results-driven business environment.
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.	Help Desk Ticket Creation; Self-Service Tools; Dashboard with Filter Options; SMS Notification System; SLA Management; Ticket Management; ITIL Processes; 3rd Party Integration.
Statistics
GitHub Stars -	GitHub Stars 858
GitHub Forks -	GitHub Forks 451
Stacks 1.7K	Stacks 22
Followers 2.8K	Followers 44
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	Jira JavaScript Perl

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Jira

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Bugify

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