Apache Kafka vs. IBM App Connect

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.

IBM App Connect Enterprise is well-suited for high-volume enterprise systems that demand robust scalability and high reliability. It excels at hybrid connectivity by easily integrating various on-premises systems using its extensive catalog of connectors. It is also ideal for workflows that require a balance between efficient low-code and the incorporation of complex, custom Java logic. On the other hand, the platform is less appropriate in scenarios requiring high development autonomy, as installation and configuration at the server level is excessively complex, creating a strong dependency on other infrastructure teams. It is also not ideal if detailed traceability and monitoring of workflows must be seamlessly managed within the tool itself, as this is difficult to manage directly in ACE. IBM App Connect Enterprise está bien adaptado para sistemas empresariales de alto volumen que exigen una escalabilidad robusta y alta confiabilidad. Sobresale en la conectividad híbrida al integrar fácilmente diversos sistemas locales (on-premises) utilizando su amplio catálogo de conectores. Además, es ideal para flujos que requieren un equilibrio entre el low-code eficiente y la incorporación de lógica Java compleja y personalizada. Por otro lado, la plataforma es menos apropiada en escenarios donde se requiere una alta autonomía de desarrollo, debido a que la instalación y configuración a nivel de servidor es excesivamente compleja , lo cual crea una fuerte dependencia de otros equipos de infraestructura. Tampoco es ideal si la trazabilidad detallada y el monitoreo de los flujos deben gestionarse sin problemas dentro de la propia herramienta, ya que esto resulta difícil de manejar directamente en ACE. Parts of this review were originally written in Spanish and have been translated into English using a third-party translation tool. While we strive for accuracy, some nuances or meanings may not be perfectly captured.

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

• Just the ability to display and consume data through a single dashboard makes this a great application for our business purposes.
• With the ability to consume their exposed API, data validation and manipulation becomes a breeze.

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

• modernization made easy, when a modernized project is created by importing a java code.
• more pre-built connectors
• watsonx code assistance for ESQL capability.

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

Incentivized

It is the best on-premise application to cloud integration in the market. I guess IBM is planning to integrate IBM App Connect with the IBM API Connect solution.

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

You can do some really powerful things with this system. The overall design is an attempt to make configurable some of the routine tasks/common functionality, but allow for development/customization of the core of the application.

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

Usually, the IBM Ops team provides a resolution or a response for 80% of defects raised in my project. There is one which has been open on their end for more than 3 months. With literally no response even after multiple follow-ups.

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

IBM App Connect started as MQSeries Integrator (MQSI) more than 20 years ago. In the IT environment, this is like an eternity. And this allowed a lot of customer experience and needs to be embedded in the product. Without it becoming a legacy application. The changes done in the latest version are preparing it for a Cloud/containers world without losing the previously learned knowledge.

• 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

• We never implemented Cast Iron in production. When compared to five competing products in the iPaaS space it didn't make it past the first few test scenarios we threw at it.

Incentivized