Kafka Manager vs Airflow | What are the differences?

Overview

Kafka Manager

Stacks69

Followers173

Votes1

Airflow

Stacks1.7K

Followers2.8K

Votes128

Airflow vs Kafka Manager: What are the differences?

Developers describe Airflow as "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. On the other hand, Kafka Manager is detailed as "A tool for managing Apache Kafka, developed by Yahoo". This interface makes it easier to identify topics which are unevenly distributed across the cluster or have partition leaders unevenly distributed across the cluster. It supports management of multiple clusters, preferred replica election, replica re-assignment, and topic creation. It is also great for getting a quick bird’s eye view of the cluster.

Airflow can be classified as a tool in the "Workflow Manager" category, while Kafka Manager is grouped under "Message Queue".

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, Kafka Manager provides the following key features:

Manage multiple clusters
Easy inspection of cluster state (topics, brokers, replica distribution, partition distribution)
Run preferred replica election

Airflow and Kafka Manager are both open source tools. It seems that Airflow with 12.7K GitHub stars and 4.62K forks on GitHub has more adoption than Kafka Manager with 7.45K GitHub stars and 1.82K GitHub forks.

Airbnb, 9GAG, and Square are some of the popular companies that use Airflow, whereas Kafka Manager is used by Yahoo!, IgnitionOne, and Ocado Technology. Airflow has a broader approval, being mentioned in 70 company stacks & 30 developers stacks; compared to Kafka Manager, which is listed in 8 company stacks and 4 developer stacks.

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Advice on Kafka Manager, Airflow

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

Kafka Manager	Airflow
This interface makes it easier to identify topics which are unevenly distributed across the cluster or have partition leaders unevenly distributed across the cluster. It supports management of multiple clusters, preferred replica election, replica re-assignment, and topic creation. It is also great for getting a quick bird’s eye view of the cluster.	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.
Manage multiple clusters;Easy inspection of cluster state (topics, brokers, replica distribution, partition distribution);Run preferred replica election;Generate partition assignments (based on current state of cluster);Run reassignment of partition (based on generated assignments)	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.
Statistics
Stacks 69	Stacks 1.7K
Followers 173	Followers 2.8K
Votes 1	Votes 128
Pros & Cons
Pros 1 Better Insights for Kafka cluster	Pros 53 Features 14 Task Dependency Management 12 Beautiful UI 12 Cluster of workers 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
Integrations
Kafka	No integrations available

What are some alternatives to Kafka Manager, Airflow?

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