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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Fiserv Wealth Management Network
Score 8.0 out of 10
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The Wealth Management Network from Fiserv is an expansive networks of managed account data and connectivity. It brings together asset managers and sponsors to perform all necessary business functions to support the industry. The Network includes messaging services, information exchange, and managed account data services.
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.
Fiserv Wealth Management Network is well suited in scenarios where an organization has multiple financial services providers and would like the benefit of tying that all together and managing it from a single, unified solution. The benefit there is increased efficiency and productivity, especially when compared to the manual alternative of having managers log in to separate sponsor platforms.
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
Cost ... it's definitely not the cheapest option out there. the way pricing is itemized and a separate upcharge for each add-on gets annoying. They can probably bundle things a little better and offer a discount on the bundled products
the volume of system updates and changes is nauseating at times and we have to prepare and test accordingly every time there is a scheduled update. that takes up a lot of resources; quarterly updates to products would be much easier.
more responsive support and ideally more stable support engineers ... it seems like turnover is high in their support roles and that impacts customer success and MTTR.
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.
Speed of service is greatly improved across partners. I believe our MTTR for service requests has improved by 30-40%
Standardized platform for our staff which translates to better retention and a lower training effort.
Cost is the negative impact ... it goes without saying that using Fiserv is much more expensive to the manual alternative which is very human resource intensive but low cost. But that's to be expected ...we're just trading time for money.
