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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Red Hat JBoss EAP
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Apache Kafka
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Chose Red Hat JBoss Enterprise Application Platform
We decided to use Red Hat JBoss EAP as it lowers our overall cost, supports all the features that we are looking for including clustering, distributed caching and web services. JBoss EAP is modular and has cloud-ready architecture.
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.
JBoss EAP is subscription based/open source platform. It's very reliable and great for deploying high transaction Java based enterprise applications. It integrates well with third party components like mod_cluster and supports popular Java EE web-based frameworks such as Spring, Angular JS, jQuery Mobile, and Google Web Toolkit.
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).
MOD_CLUSTER integration. JBoss EAP integrates pretty well with mod_cluster. This is an intelligent load balancer especially useful in highly clustered environments.
Supports enterprise-grade features such as high availability clustering, distributed caching, messaging etc.
Supports deployment in on-premise, virtual and hybrid cloud environments.
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
Jboss CLI is a great tool but we had trouble using it to get values that are displayed on Jboss GUI. It also has limitations parsing the applications.xml files and we had to use a mix of jboss-cli and linux bash commands to automate certain application administrative tasks.
JBoss doesn't really provides performance tuning recommendations. It would have been nice if it could learn from the current demand vs current settings for things like connection pool, server configurations, garbage collection etc.
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
Usually, Red Hat JBoss Enterprise Application Platform is good at performance and well suited for high traffic Java EE-based applications, but we have faced hard times performance tuning it for our specific needs. The product would be nicer if they would add a performance diagnostic and recommendations feature to it.
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.
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.
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.
