Amazon Kinesis vs. Confluent

Perfect for real-time data processing and streaming. Also, there's no need for any specific setup - you just start using it immediately and it easily integrates with the rest of AWS capabilities (like Redshift), although integration with Lambda could be better. You can make your overall analytics landscape way simpler with Kineses even if you have non-Amazon solutions like Tableau. It all integrates really well!

Incentivized

If your company needs to build event-driven applications, like in healthcare industry, you need to enhance interoperability, and you are seeking a reliable service with enterprise-grade support, Confluent is the best on the market you can get. Their product works great and they provide very good customer service.

Incentivized

• Integrating with other Amazon services
• Scaling requests
• Totally serverless platform
• Simple management

Incentivized

• Products work great.
• Training is available.
• Customer support is good.

Incentivized

• Improve integration with AWS Lambda
• Some duplicate records coming from the stream

Incentivized

• Cloud based Azure platform features for Confluent lacks behind AWS And GCP

Incentivized

The documentation was confusing and lacked examples. The streams suddenly stopped working with no explanation and there was no information in the logs. All these were more difficult when dealing with enhanced fan-out. In fact, we were about to abort the usage of Kinesis due to a misunderstanding with enhanced fan-out.

Incentivized

The support from the Confluent platform is great and satisfying. We have been working with Confluent for more than a year now. They sent out resident architects to help us set up Confluent cluster on our cloud and help us troubleshoot problems we have encountered. Overall, it has been a great experience working with the Confluent Platform.

Incentivized

Kinesis is oriented to streaming in a scalable way large volumes of information in real-time. Glue is more an ETL so it is not well suited for real-time applications while Beanstalk is more a simple container platform. Lambda could do the job but it would require a lot of programming to accomplish the same as Kinesis. In fact, our solution employed the four elements for different tasks but using Kinesis as the message bus.

Incentivized

For our use case it was very important that the technology we were working with fit into our Azure architecture, and met our data processing size requirements to stream data within certain SLAs. Confluent more than met our performance requirements and compared to the others scale options and cost to run it was more than financially viable as a platform solution to our global operations.

Incentivized

• Caused us to need to re-engineer some basic re-try logic
• Caused us to drop some content without knowing it
• Made monitoring much more difficult
• We eventually switched back to SQS because Kinesis is not the same as a Queue system

Incentivized

• Confluent replaced Microsoft Event Hubs in our environment and unblocked data platform services rollout globally.

Incentivized