Apache Kafka vs. Software AG Universal Messaging

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.

Software AG Universal Messaging is best used in conjunction with Software AG webMethods integration platform, as it works seamlessly with other components of that platform such as webMethods Integration Server and webMethods Trading Networks. It can be used with any messaging client developed in Java or .NET and supports JMS clients, however, it is best suited to being used as one component in an organization that is going to implement the entire webMethods platform to implement their Enterprise Service Bus integration solution.

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

• Low latency high performance messaging and event handling
• High availability
• Horizontal scalability
• Seamless integration with Software AG webMethods integration platform

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

• Logging
• Administration
• Observability

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

Software AG Universal Messaging has excellent performance characteristics, a good high availability story, and works great when you are using the other products in the Software AG webMethods integration platform, as our organization is. It has relatively high licensing costs, which is a large negative when compared with open-source solutions such as Apache Kafka. However, Software AG provides good support services, and is a market leader in the integration space, giving some surety for risk-averse organizations that if something goes wrong we have a vendor we can rely on to help fix it.

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

• Enables disparate software systems to be integrated, reducing manual data entry
• Enables high performance event handling for data lakes and analytics
• Has relatively high licensing costs

Incentivized