Apache Kafka vs. Apache Pulsar vs. Google Cloud IoT

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.

I consider myself very techy and found Google Cloud IoT platform very challenging to manage. The lack of tutorials and discussions to understand how each section works is very challenging. I specifically made a connection after several hours to Google Nest to third-party integration, Home Assistant. Shortly after Google Cloud upgraded to a new version breaking the connection. This was extremely frustrating. No service should take several hours to figure out, in my opinion, if it does, the platform is doing a poor job of making it easy. I'm personally very discouraged any time I ever have to use this platform. It's very hard to find answers.

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

• Integration with different brands of microcontrollers including the one currently used by Espresiff.
• The platform is very robust and secure.

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

• Not beginner friendly.
• Needs more tutorials and walk throughs on how to get started.
• Very complicated and unless you know what you are doing you will be lost.
• Lack of material found on how to do each section / services.

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

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

Although comparisons are hateful, even more so when we are talking about leading brands where the quality of their services are indisputable, the general environment of Google was more familiar to me since I use, for example, Google Firebase on a daily basis, where part of the concepts are similar, without Without a doubt, AWS services are excellent, but it was easier for me to go through the functions of Google Cloud IoT.

Incentivized

• 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

• The amount of hours to get things integrated is negative.
• The amount of hours researching how to get devices integrated is negative.
• Overall the amount of time and effort getting things working has been a negative experience.

Incentivized