CloudAMQP vs Kafka: What are the differences?
Developers describe CloudAMQP as "RabbitMQ as a Service". Fully managed, highly available RabbitMQ servers and clusters, on all major compute platforms. On the other hand, Kafka is detailed as "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.
CloudAMQP and Kafka belong to "Message Queue" category of the tech stack.
Some of the features offered by CloudAMQP are:
- Support - 24/7 support, via email, chat and phone.
- Real time metrics and alarms - Get notified in advanced when your queues are growing faster than you're consuming them, when you're servers are over loaded etc. and take action before it becomes a problem.
- Auto-healing - Our monitoring systems automatically detects and fixes a lot of problems such as kernel bugs, auto-restarts, RabbitMQ/Erlang version upgrades etc.
On the other hand, Kafka provides the following key features:
- 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)
"Some of the best customer support you'll ever find" is the top reason why over 3 developers like CloudAMQP, while over 95 developers mention "High-throughput" as the leading cause for choosing Kafka.
Kafka is an open source tool with 12.7K GitHub stars and 6.81K GitHub forks. Here's a link to Kafka's open source repository on GitHub.
According to the StackShare community, Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to CloudAMQP, which is listed in 12 company stacks and 5 developer stacks.
What is CloudAMQP?
What is Kafka?
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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.