We performed a comparison between Amazon Kinesis and Spring Cloud Data Flow based on real PeerSpot user reviews.
Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."From my experience, one of the most valuable features is the ability to track silent events on endpoints. Previously, these events might have gone unnoticed, but now we can access them within the product range. For example, if a customer reports that their calls are not reaching the portal files, we can use this feature to troubleshoot and optimize the system."
"Great auto-scaling, auto-sharing, and auto-correction features."
"The scalability is pretty good."
"I have worked in companies that build tools in-house. They face scaling challenges."
"Kinesis is a fully managed program streaming application. You can manage any infrastructure. It is also scalable. Kinesis can handle any amount of data streaming and process data from hundreds, thousands of processes in every source with very low latency."
"Amazon Kinesis has improved our ROI."
"What I like about Amazon Kinesis is that it's very effective for small businesses. It's a well-managed solution with excellent reporting. Amazon Kinesis is also easy to use, and even a novice developer can work with it, versus Apache Kafka, which requires expertise."
"Its scalability is very high. There is no maintenance and there is no throughput latency. I think data scalability is high, too. You can ingest gigabytes of data within seconds or milliseconds."
"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."
"Kinesis Data Analytics needs to be improved somewhat. It's SQL based data but it is not as user friendly as MySQL or Athena tools."
"One thing that would be nice would be a policy for increasing the number of Kinesis streams because that's the one thing that's constant. You can change it in real time, but somebody has to change it, or you have to set some kind of meter. So, auto-scaling of adding and removing streams would be nice."
"It would be beneficial if Amazon Kinesis provided document based support on the internet to be able to read the data from the Kinesis site."
"Something else to mention is that we use Kinesis with Lambda a lot and the fact that you can only connect one Stream to one Lambda, I find is a limiting factor. I would definitely recommend to remove that constraint."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"For me, especially with video streams, there's sometimes a kind of delay when the data has to be pumped to other services. This delay could be improved in Kinesis, or especially the Kinesis Video Streams, which is being used for different use cases for Amazon Connect. With that improvement, a lot of other use cases of Amazon Connect integrating with third-party analytic tools would be easier."
"I think the default settings are far too low."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"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."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."
"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
Amazon Kinesis is ranked 1st in Streaming Analytics with 24 reviews while Spring Cloud Data Flow is ranked 9th in Streaming Analytics with 5 reviews. Amazon Kinesis is rated 8.0, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Amazon Kinesis writes "Used for media streaming and live-streaming data". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Provides ease of integration with other cloud platforms ". Amazon Kinesis is most compared with Azure Stream Analytics, Amazon MSK, Confluent, Apache Flink and Apache Pulsar, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, TIBCO BusinessWorks and StreamSets. See our Amazon Kinesis vs. Spring Cloud Data Flow report.
See our list of best Streaming Analytics vendors.
We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.