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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Oracle SOA Suite
Score 8.0 out of 10
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The rapid adoption of cloud-based applications by the enterprise, combined with organizations’ desire to integrate applications with mobile technologies, is dramatically increasing application integration complexity. Oracle SOA Suite 12c, the latest version of the company's unified application integration and SOA solution, offers a simplified cloud, mobile, on-premises and Internet of Things (IoT) integration capabilities within a single platform.
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).
In comparison to Open source products like Apache Camel and Mule ESB, Oracle ESB is more robust and offers better enterprise capabilities. However, the licensing costs are fairly prohibitive and are preventing widespread product adoption. At our university, we had already purchased the Oracle Campus Solutions ERP suite and hence had little problems integrating their OSB as well.
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.
We have had not many issues with Oracle Service Bus and it's very stable for our requirements. It's highly available and helps us implement Tier1 applications on it.
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
It's an excellent enterprise service bus and has very stable features. We have been using it since 2008. We did hit into some issues. But, recreating the service helped fix many issues. Also, deployment to various environments was easy. Also, the plugin on Eclipse helps to build proxy and business services quick and easy.
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.
We had some issues with MQ connectivity through OSB and our experience was poor with the support team. They do respond. But, it felt like we are ignored and we had bad support. We had to escalate and things used to get dragged for weeks before we get more quality questions on how to pursue investigation.
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.
Mule ESB is an open source tool and would definitely cost less, however is not as sophisticated a product for the business functionality we need at US Cellular.. I have reviewed IBM WebSphere Message Broker, is very cumbersome and not very user friendly. Despite some of the license cost concerns, Oracle Service Bus stands out as an ideal Enterprise Service Bus solution at US Cellular
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.
