Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java. The Kafka event streaming platform is used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
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IBM App Connect
Score 9.5 out of 10
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IBM’s App Connect is a cloud-based data integration platform with data mapping and transformation capabilities within connectors between high-volume systems. App Connect also offers near-real time data synchronization and an API builder that is adaptable to the user’s coding skill level.
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Pricing
Apache Kafka
IBM App Connect
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Apache Kafka
IBM App Connect
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Apache Kafka
IBM App Connect
Features
Apache Kafka
IBM App Connect
Cloud Data Integration
Comparison of Cloud Data Integration features of Product A and Product B
For brokering messages, Confluent Kafka is well suited since it offers a managed solution ready to use. Scenarios where the solution is not very well suited are for example, where pricing is an issue. The solution costs quite a lot for basic usage (for example: for 3 clusters, pricing is above 100k$ a year).
IBM App Connect is well-suited to serve as a central integration hub, particularly for scenarios involving data transformation, complex routing logic, and dynamic backend routing. It excels at enabling legacy system modernization and supports real-time, event-driven architectures effectively. However, it is less appropriate for simple point-to-point integrations or for use cases requiring workflow process management and human task orchestration, where BPM or lightweight automation tools may be more suitable.
Apache Kafka is able to handle a large number of I/Os (writes) using 3-4 cheap servers.
It scales very well over large workloads and can handle extreme-scale deployments (eg. Linkedin with 300 billion user events each day).
The same Kafka setup can be used as a messaging bus, storage system or a log aggregator making it easy to maintain as one system feeding multiple applications.
The Kafka Tool is a community-made Java application that looks and feels from the past century.
Logging can be confusing. This certainly shows when we have to do troubleshooting.
Hybrid scenarios - pub/sub, but there are services in and outside a Kubernetes cluster. Then there are a ~3 options, but only 2 (the harder ones) are production-safe.
Kafka has suited our use case very well so far. Going forward we are planning to expand our platform manifold so the load on Kafka and our reliance on Kafka is going to increase only.
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.
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
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.
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.
Apache Kafka is built for scale. From high throughput and real-time data streaming, it has a strong advantage over RabbitMQ with its low latency. This put Apache Kafka at the forefront as the platform of choice for large datasets messaging and ensuring scalability when data scale up tremendously. RabbitMQ however has its strengths in traditional messaging. Routing and message delivery reliability are the bedrock of RabbitMQ and this is where RabbitMQ excels. In my previous workplace, RabbitMQ was of choice as reliability matters more than scale. In two words. Apache Kafka for scale, RabbitMQ for reliability. And for cloud deployment and large dataset messaging in what I am doing now, Apache Kafka is the default choice.
We selected IBM App Connect Enterprise due to our confidence in IBM as a long-term partner and our history with their integration technology (Message Broker/Bus). IBM App Connect provides the robustness and high reliability needed for our core on-premises systems, with proven scalability to the cloud. Its key strength is enabling deep integration by combining low-code with the power of complex, custom logic, ensuring the platform's capabilities exactly matched our need to handle complex flows.
Positive: bursts of traffic on special holidays are easy to handle because Kafka can absorb and buffer all the messages we need to process long enough to let an understaffed set of back-end services catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines running.
Positive: makes decoupling the web and API services from the deeper back-end services easier by providing topics as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
Negative: our engineers have made mistakes such as accidentally dropping a few thousand messages due to the CLI being confusing to use, and as a result a customer lost some of their precious data. I'd say that was more our fault than Kafka's though.
