We performed a comparison between Confluent and Spring Cloud Data Flow based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon Web Services (AWS), Databricks, Microsoft and others in Streaming Analytics."The solution can handle a high volume of data because it works and scales well."
"I find Confluent's Kafka Connectors and Kafka Streams invaluable for my use cases because they simplify real-time data processing and ETL tasks by providing reliable, pre-packaged connectors and tools."
"It is also good for knowledge base management."
"One of the best features of Confluent is that it's very easy to search and have a live status with Jira."
"Confluence's greatest asset is its user-friendly interface, coupled with its remarkable ability to seamlessly integrate with a vast range of other solutions."
"Kafka Connect framework is valuable for connecting to the various source systems where code doesn't need to be written."
"I would rate the scalability of the solution at eight out of ten. We have 20 people who use Confluent in our organization now, and we hope to increase usage in the future."
"The design of the product is extremely well built and it is highly configurable."
"There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure."
"The most valuable features of Spring Cloud Data Flow are the simple programming model, integration, dependency Injection, and ability to do any injection. Additionally, auto-configuration is another important feature because we don't have to configure the database and or set up the boilerplate in the database in every project. The composability is good, we can create small workloads and compose them in any way we like."
"The most valuable feature is real-time streaming."
"The product is very user-friendly."
"It could have more integration with different platforms."
"Confluent has a good monitoring tool, but it's not customizable."
"In Confluent, there could be a few more VPN options."
"It could be improved by including a feature that automatically creates a new topic and puts failed messages."
"There is no local support team in Saudi Arabia."
"They should remove Zookeeper because of security issues."
"Currently, in the early stages, I see a gap on the security side. If you are using the SaaS version, we would like to get a fuller, more secure solution that can be adopted right out of the box. Confluence could do a better job sharing best practices or a reusable pattern that others have used, especially for companies that can not afford to hire professional services from Confluent."
"The formatting aspect within the page can be improved and more powerful."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."
"Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications."
"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
Confluent is ranked 4th in Streaming Analytics with 19 reviews while Spring Cloud Data Flow is ranked 9th in Streaming Analytics with 5 reviews. Confluent is rated 8.4, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Confluent writes "Has good technical support services and a valuable feature for real-time data streaming ". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Provides ease of integration with other cloud platforms ". Confluent is most compared with Amazon MSK, Amazon Kinesis, Databricks, AWS Glue and Oracle GoldenGate, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, Azure Data Factory and Databricks.
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