We performed a comparison between Amazon Kinesis and Spring Cloud Data Flow based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Confluent and others in Streaming Analytics."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."
"I find almost all features valuable, especially the timing and fast pace movement."
"What turns out to be most valuable is its integration with Lambda functions because you can process the data as it comes in. As soon as data comes, you'll fire a Lambda function to process a trench of data."
"Amazon Kinesis has improved our ROI."
"The management and analytics are valuable features."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"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."
"The scalability is pretty good."
"The product is very user-friendly."
"The most valuable feature is real-time streaming."
"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."
"Kinesis can be expensive, especially when dealing with large volumes of data."
"The solution has a two-minute maximum time delay for live streaming, which could be reduced."
"If there were better documentation on optimal sharding strategies then it would be helpful."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"In order to do a successful setup, the person handling the implementation needs to know the solution very well. You can't just come into it blind and with little to no experience."
"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."
"Snapshot from the the from the the stream of the data analytic I have already on the cloud, do a snapshot to not to make great or to get the data out size of the web service. But to stop the process and restart a few weeks later when I have more data or more available of the client teams."
"Lacks first in, first out queuing."
"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."
"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."
"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 2nd in Streaming Analytics with 21 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, Apache Flink, Amazon MSK, Confluent and PubSub+ Event Broker, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, Azure Data Factory and Informatica PowerCenter.
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.
