Apache Kafka vs. Apache Pulsar vs. Microsoft Azure IoT Edge

Apache Kafka is well-suited for most data-streaming use cases. Amazon Kinesis and Azure EventHubs, unless you have a specific use case where using those cloud PaAS for your data lakes, once set up well, Apache Kafka will take care of everything else in the background. Azure EventHubs, is good for cross-cloud use cases, and Amazon Kinesis - I have no real-world experience. But I believe it is the same.

Deployment of mission critical business support solutions particularly those that support virtual learning and video conferencing environments. This is because they help organisations such as the university to effectively transition to a digitally compliant organisation.

Incentivized

• Really easy to configure. I've used other message brokers such as RabbitMQ and compared to them, Kafka's configurations are very easy to understand and tweak.
• Very scalable: easily configured to run on multiple nodes allowing for ease of parallelism (assuming your queues/topics don't have to be consumed in the exact same order the messages were delivered)
• Not exactly a feature, but I trust Kafka will be around for at least another decade because active development has continued to be strong and there's a lot of financial backing from Confluent and LinkedIn, and probably many other companies who are using it (which, anecdotally, is many).

Incentivized

• It works even if there is no internet at client location. It will record all the generated data and only send them to cloud storage when there is internet connection.
• It allows you to bring cloud functionality down to Edge location in the form of docker container.
• It also supports two way communication from device to cloud and vice-versa.

Incentivized

• Sometimes it becomes difficult to monitor our Kafka deployments. We've been able to overcome it largely using AWS MSK, a managed service for Apache Kafka, but a separate monitoring dashboard would have been great.
• Simplify the process for local deployment of Kafka and provide a user interface to get visibility into the different topics and the messages being processed.
• Learning curve around creation of broker and topics could be simplified

• For some reason, the containerized architecture was also making this very hard
• Had some issues when debugging edge modules, needs a professional grade remote debugging
• Industrial automation still requires attention. OPC-UA was good but Modbus or custom PLCs etc was painful to set up

Incentivized

Kafka is quickly becoming core product of the organization, indeed it is replacing older messaging systems. No better alternatives found yet

Incentivized

Apache Kafka is highly recommended to develop loosely coupled, real-time processing applications. Also, Apache Kafka provides property based configuration. Producer, Consumer and broker contain their own separate property file

Incentivized

Most of the time it works perfectly, does not require any attention, but when there is a problem, it's really hard to diagnose it. I think the documentation for the positive scenarios are good on the Microsoft side, but for negative scenarios, I don't think there is enough community help out there, in my opinion.

Incentivized

Support for Apache Kafka (if willing to pay) is available from Confluent that includes the same time that created Kafka at Linkedin so they know this software in and out. Moreover, Apache Kafka is well known and best practices documents and deployment scenarios are easily available for download. For example, from eBay, Linkedin, Uber, and NYTimes.

Incentivized

I used other messaging/queue solutions that are a lot more basic than Confluent Kafka, as well as another solution that is no longer in the market called Xively, which was bought and "buried" by Google. In comparison, these solutions offer way fewer functionalities and respond to other needs.

Incentivized

The reason Behind this is the cost, as Microsoft provides solutions and integrations of services cheaper than AWS.

• Positive: Get a quick and reliable pub/sub model implemented - data across components flows easily.
• Positive: it's scalable so we can develop small and scale for real-world scenarios
• Negative: it's easy to get into a confusing situation if you are not experienced yet or something strange has happened (rare, but it does). Troubleshooting such situations can take time and effort.

Incentivized

• Very valuable return on investment for IT capex committed to the deployment
• Improvements in overall organisational efficiency and agility
• Repositioning the organisation to become digitally transformed

Incentivized