Apache Kafka on Confluent Cloud vs JFrog DevOps Cloud Platform comparison

"The state-saving feature is very much appreciated. It allows me to rewind a certain process if I see an error and then reprocess it."

"I use it for real-time processing workloads. So, in some instances, it's like IoT data. We need to put it into a data lake."

"In case of huge transactions on the web or mobile apps, it helps you capture real-time data and analyze it."

"The product's installation phase is pretty straightforward for us since we know how to use it."

"Overall, I think it's a good experience. Apache Kafka can be quite complex and difficult to maintain on your own, so using Apache Kafka on Confluent Cloud makes it much easier to use it without worrying about setup and maintenance."

"Kafka provides handy properties that allow us to directly configure the data, whether to keep it or discard it after use."

"Kafka and Confluent Cloud have proven to be cost-effective, especially when compared to other tools. In a recent BI integration program over the past year, we assessed multiple use cases spanning ship-to-shore and various Azure integrations. Our findings revealed that Confluent Kafka performed exceptionally well, standing out alongside Genesys and Azure Event Hubs. While these three are top contenders, the choice among other tools depends on the specific use case and project requirements. The customer initially used tools like SMQs, FITRA, and Stream for real-time data processing. However, after our recommendation, Confluent Cloud proved to be a superior choice, capable of replacing these three tools and simplifying their data infrastructure. This shift to a single tool, Confluent Cloud, streamlined their operations, making maintenance and management more efficient for their internal projects."

"Confluent Cloud handles data volume pretty well."

"The most valuable features include task tracking and reporting capabilities."

"They have a professional service team that works alongside their engineering and performance teams."

"I appreciate the features in JFrog DevOps Cloud Platform, especially the efficient file management where downloads and uploads are optimized, saving time. The storage efficiency is also great as it avoids redundancy, which is crucial for our team. It is also quite easy to use, especially for basic commands through the command line. It's straightforward for us internally, and our data is well-hosted on their servers, which makes data location and querying fast and efficient. Moving our storage to JFrog has streamlined our development cycle by eliminating duplicated data, which previously took up extra space locally. This efficiency is crucial for our workflow, although network speeds still play a significant role in performance."

"The solution is expensive."

"Regarding real-time data usage, there were challenges with CDC (Change Data Capture) integrations. Specifically, with PyTRAN, we encountered difficulties. We recommended using our on-premises Kaspersky as an alternative to PyTRAN for that specific use case due to issues with CDC store configuration and log reading challenges with the iton components."

"Maybe in terms of Apache Kafka's integration with other Microsoft tools, our company faced some challenges."

"For the original Kafka, there is room for improvement in terms of latency spikes and resource consumption. It consumes a lot of memory."

"There could be an in-built feature for data analysis."

"There are some premium connectors, for example, available in Confluent, which you cannot access in the marketplace, so there are some limitations."

"The administration port could be more extensive."

"There's one thing that's a common use case, but I don't know why it's not covered in Kafka. When a message comes in, and another message with the same key arrives, the first version should be deleted automatically."

"Our locations are in different environments, so the remote server takes time to catch up, causing replication delays. The engineering team suggested that this issue would be resolved, but I'm not sure if it has been addressed yet. This is more of a feature enhancement that we suggested."

"The product could benefit from enhanced integration capabilities with older software systems and more customizable reporting options."

"We have encountered stability issues lately, particularly with frequent 500 internal server errors. Despite efforts from our DevOps team to adjust settings, these issues persist, affecting our workflow, especially with machine learning data uploads. Overall, while it's beneficial for storage and accessibility, stability issues need improvement for seamless operations. The occasional occurrence of internal server errors takes several minutes to resolve on their own and can disrupt workflows. Another concern is that sometimes files appear to be successfully uploaded, but then they cannot be downloaded, with no error message indicating the issue during the upload process. This inconsistency needs to be addressed by JFrog to ensure reliable functionality for users like us."

"Regarding pricing, Apache Kafka on Confluent Cloud is not a cheap tool. The right use case would justify the cost. It might make sense if you have a high volume of data that you can leverage to generate value for the business. But if you don't have those requirements, there are likely cheaper solutions you could use instead."

"I consider that the product's price falls under the middle range category."

"It's quite affordable considering the value it provides."

"I think the pricing is fair, but Confluent requires a little bit more thinking because the price can go up really quickly when it comes to premium connectors."

"The product pricing is competitive but worth negotiating for volume discounts or longer-term contracts."

"Regarding pricing, I focus on the platform's interface and user communication rather than costs."