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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Aurea Process
Score 5.0 out of 10
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Aurea Process (formerly CX Process) from Aurea Software in Austin is a business process management offering, based on Savvion BPM.
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.
The tool has potential. Its capabilities and visual aspects could be considered rather basic but this might improve, particularly if the business intelligence/analytics aspect is leveraged. Once running well, it could allow (perhaps smaller) companies to successfully improve their customers' experiences through digitalizing customer journey - and we all know that customer loyalty goes a long way. However, whether or not the tool is comprehensive enough to deliver this for larger companies with more complex, multi- and omni-channel interactions is yet to be seen...
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).
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
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
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.
As our customers vary in size and maturity, the ROI ranges accordingly.
For younger, smaller businesses this is a useful tool. Digitalization of he customer journey has certainly helped save time and efforts in many cases.
For more mature market players the tool is not always comprehensive enough. Dashboard and report personalization take time and efforts, and sometimes it feels that a dedicated BI tool would be a more suitable solution.
