IBM Confluent vs. Mage AI

If you have a need to stream data, real time or segmented structured data then Confluent is a great platform to do so with. You won't run into packet transfer size limitations that other platforms have. Flexibility in on-prem, cloud, and managed cloud offerings makes it very flexible no matter how you choose to implement.

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Mage is well-suited for probability score for uptake of every product is calculated for customers using ML/ Regression models, choosing customers for a product/ Top products for a customer, based on the requirement and Identifying popular product combinations using association rules from Market Basket Analysis (or affinity Analysis)\Bundle these products as combos.

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

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• Ranking algorithms.
• Cloud-based tool.
• Increase user engagement.

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

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• Acquisition Contribution.
• Business Intelligence Reporting.
• Data Destinations.

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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.

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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.

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Mage was the easiest in terms of ease of implementation due to its no-code functionality. However, Mage doesn't have a whole ecosystem like AWS and slightly falls behind there.

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• It enables us to develop event driven application.
• It increases our ability to handle streaming data.
• It reduces latency of communication.

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• Business Understanding.
• Data Acquisition and Understanding.
• Data Modeling and Evaluation.

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info