Apache Kafka vs. Kibana

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.

Kibana is indeed a powerful tool and has many use cases especially in environments that rely heavily on real-time log analysis and visualisation. Kibana’s ability to handle large volumes of log data and present it in an accessible, searchable format is invaluable. We use Kibana to monitor security related issues and it proactively alerts our Slack channels about any anomality or issues.

Incentivized

• 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).

Incentivized

• Fast searches with powerful index.
• Beautiful data visualizations.
• Real-time observability.

Incentivized

• 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

• Some performance issues with large datasets.
• Linking to dashboards makes extremely long urls.
• Lack of reports.

Incentivized

Kafka is quickly becoming core product of the organization, indeed it is replacing older messaging systems. No better alternatives found yet

Incentivized

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

Incentivized

Its usability is generally good and it provides teams with a basic to intermediate understanding about data visualization. It is very user-friendly when it comes to creating dashboards. The UI is very good and simple. Its integration with other tools for alerting and reporting is amazing. But its advance features have a learning curve and a first timer needs some time to use the advance features.

Incentivized

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.

Incentivized

We did not use the official Kibana support. Documentation was easy enough to follow.

Incentivized

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.

Incentivized

Kibana is free; it was the first and only thing we've tried in this area.

Incentivized

• 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.

Incentivized

• Issues that affect checkout experiences for customers are able to be prioritized and solved quickly.
• We are able to more efficiently use resources due to the automation of reporting alerts. Decreasing employee resources needed.
• Visualization allows us to quickly share issues and explain to coworkers in order to escalate issues that can cost our bottom line.

Incentivized