Kafka vs Kafka Manager: What are the differences?
Kafka: Distributed, fault tolerant, high throughput pub-sub messaging system. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design; Kafka Manager: 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.
Kafka and Kafka Manager belong to "Message Queue" category of the tech stack.
Some of the features offered by Kafka are:
- Written at LinkedIn in Scala
- Used by LinkedIn to offload processing of all page and other views
- Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled)
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
Kafka and Kafka Manager are both open source tools. It seems that Kafka with 12.7K GitHub stars and 6.81K forks on GitHub has more adoption than Kafka Manager with 7.55K GitHub stars and 1.84K GitHub forks.
According to the StackShare community, Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to Kafka Manager, which is listed in 8 company stacks and 5 developer stacks.
What is Kafka?
What is Kafka Manager?
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Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.