Apache Kafka vs Apigee Edge

• 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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• Better exception handling with the Raise exception policies help to monitor the flow by setting up the flow conditions.
• Easy development of a Proxy and APIs with much less tutoring and helps make getting started for new users easy.
• Very good documentation and blog with details of most common failures and error handling.
• A very very easy to use console.

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• The Kafka Tool is a community-made Java application that looks and feels from the past century.
• Logging can be confusing. This certainly shows when we have to do troubleshooting.
• Hybrid scenarios - pub/sub, but there are services in and outside a Kubernetes cluster. Then there are a ~3 options, but only 2 (the harder ones) are production-safe.

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• Only one user can be active in a proxy at a time
• No version control
• Prohibited from using JSON.stringify on Apigee objects (tokens)
• Debugging is difficult
• Unable to rename or delete policies without bumping revision
• Why would anyone give a js policy one name, display name something else, and script a different name?
• 'Trace' limited to only 20 transactions
• UI allows users to add target servers, but users must utilize the api to turn on SSL.
• I'm sure there's more, they just aren't coming to mind right now.
• Apigee forgets (expires?) your password at random intervals without notice. Every few weeks, or days, sometimes even three times in one day, I'll attempt to login to Apigee and my password will be 'wrong'. I've reset my password and Apigee still claims it's wrong. I've had to reset my password three times before it finally let me log back in.

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• Kafka makes the messaging itself more reliable (as it has the distributed storage by itself and the message doesn't disappear even after it has been consumed).
• Kafka can support a much higher volume use case, without too much extra pressure on the existed hardware.

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• As a public entity it is hard to say how much ROI we can have. We have yet to create a billing and ROI plan. We are thinking of other ways to create ROI, possibly through data/service barter.

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