Likelihood to Recommend 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.
Read full review webMethods Trading Networks is an excellent choice for a business-to-business (B2B) gateway platform, for sending and receiving messages to/from external trading partner organizations. It excels at making it easy to define new interfaces and provides robust mechanisms for ensuring successful message delivery and processing through automatic retries. We also use it successfully for all our application-to-application (A2A) integration, which many would believe is beyond the scope of what webMethods Trading Network is good for; however, we have made it work and it has been very successful in our organization. I would not recommend using webMethods Trading Network for integration that requires low-latency or high-bandwidth data transfers. It is much better suited to shipping reasonable sized XML or JSON or flat file messages (less than 100 mb) around in situations that do not need sub-second latency. If you have low-latency or high-bandwidth needs, you should use a product more focussed on eventing, such as Kafka, for example.
Read full review Pros 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). Read full review Document tracking Efficient partner management Set up trading partners with connectivity information Read full review Cons 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 Read full review Vendor is slow to add new features Performance scales with underlying database performance Use of My webMethods to administrate trading networks is clunky and slow Read full review Likelihood to Renew Kafka is quickly becoming core product of the organization, indeed it is replacing older messaging systems. No better alternatives found yet
Read full review Usability 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
Read full review Support Rating 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.
Read full review Alternatives Considered 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.
Read full review Ease of use and robustness of the product.
Read full review Return on Investment 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. Read full review Improved time to market for new A2A and B2B interfaces Ability to support modern standards with B2B partners Improved supportability and robustness when compared to the previous bespoke custom solution it replaced Read full review ScreenShots