Apache Kafka vs WSO2 Enterprise Service Bus

• Apache Kafka is able to handle a large number of I/Os (writes) using 3-4 cheap servers.
• It scales very well over large workloads and can handle extreme-scale deployments (eg. Linkedin with 300 billion user events each day).
• The same Kafka setup can be used as a messaging bus, storage system or a log aggregator making it easy to maintain as one system feeding multiple applications.

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• One of the basic requirement of an ESB product is that it should be able to support transformation. WSO2 ESB provides support of XSLT, so you can transform your request to whatever format. Moreover, transformations like converting your xml payload into JSON and JSON payload to XML are out of the box available.
• WSO2 ESB provides a scheduler feature, by which you can configure your own scheduler to call a proxy service at a particular time of day or or initiate sequence.
• WSO2 ESB provides excellent error handling techniques, WSO2 ESB provides detailed error handling scenarios to tackle all the situations. WSO2 ESB also provides custom error handling by which you can make your own custom error message before sending it back to client.

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• Still a bit inmature, some clients have required recoding in the last few versions
• New feaures coming very fast, several upgrades a year may be required
• Not many commercial companies provide support

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• While it's easy to configure for a quick start, it is not so easy to deploy by yourself in a complex production scenario.
• Not very stable for production usage, we encountered several trivial bugs that make us believe that this product is still not widely adopted.
• Lack of a built in mechanism for auto-restart in case of an application server crash.

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• Positive impact on ROI since now we can use one large deployment of Apache Kafka that can be used for multiple scenarios ( storage systems, log aggregate, messaging queue ).
• It is open-source so there are no licenses or subscription fees reducing the cost of deployment.
• Data can now be ingested and analyzed in real-time making it easy to fine-tune the customer experience and decision making for internal IT.

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• Very well documented tutorials and case studies makes it easy to learn.
• It has a really supportive community
• It is fast and it can easily handle 300 tps of average use on a VM with 4Gig RAM

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