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Amazon Kinesis vs Spring Cloud Data Flow comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Amazon Kinesis
Ranking in Streaming Analytics
2nd
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
28
Ranking in other categories
No ranking in other categories
Spring Cloud Data Flow
Ranking in Streaming Analytics
9th
Average Rating
7.8
Reviews Sentiment
6.8
Number of Reviews
9
Ranking in other categories
Data Integration (21st)
 

Mindshare comparison

As of July 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 8.0%, down from 12.6% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.8%, up from 4.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Prabin Silwal - PeerSpot reviewer
Pipeline setup is very simple
I am not exactly sure about where improvements are needed in the tool. When I was working on the tool, it was very scalable, and the only thing we needed in our company was temporary streaming stuff that could work well. We didn't want to set up our own Kafka, other queues, or processing systems. As it is a cloud tool, it is easy for us to use the tool, and it satisfies all our requirements. Maybe for the other cases, if we need, then it may need some improvements. The tool satisfies our particular needs. Currently, the pipeline setup is very simple. For our particular use cases, it is because we just want to get the data and send it to the different data lakes or some logging system. Previously, we also used Amazon Kinesis to log those to Splunk, and later on, we removed Splunk and transferred that to Datadog. For our use cases, I don't want any new features in the tool. Amazon Kinesis' use case is for collecting, processing, and analyzing. If anything can be added to the tool, then I feel one should be able to use the same kind of tool so that everything is there in the product, like an alert system, and so that one can analyze, make a query, and do sourcing from the solution itself rather than using other logging and monitoring systems. The tool should focus on having an alert system rather than having to use a third-party solution. We can just get the data over Amazon Kinesis, and we can directly use all the benefits of current analytical tools, like in the areas involving BI, Looker, and Tableau. One would not need to buy the aforementioned tools, and we can just get started with Amazon Kinesis.
NitinGoyal - PeerSpot reviewer
Has a plug-and-play model and provides good robustness and scalability
The solution's community support could be improved. I don't know why the Spring Cloud Data Flow community is not very strong. Community support is very limited whenever you face any problem or are stuck somewhere. I'm not sure whether it has improved in the last six months because this pipeline was set up almost two years ago. I struggled with that a lot. For example, there was limited support whenever I got an exception and sought help from Stack Overflow or different forums. Interacting with Kubernetes needs a few certificates. You need to define all the certificates within your application. With the help of those certificates, your Java application or Spring Cloud Data Flow can interact with Kubernetes. I faced a lot of hurdles while placing those certificates. Despite following the official documentation to define all the replicas, readiness, and liveliness probes within the Spring Cloud Data Flow application, it was not working. So, I had to troubleshoot while digging in and debugging the internals of Spring Cloud Data Flow at that time. It was just a configuration mismatch, and I was doing nothing weird. There was a small spelling difference between how Spring Cloud Data Flow was expecting it and how I passed it. I was just following the official documentation.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Amazon Kinesis has improved our ROI."
"The Kinesis VideoStream and DataStream are the most important features."
"Everything is hosted and simple."
"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."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"The solution works well in rather sizable environments."
"One of the best features of Amazon Kinesis is the multi-partition."
"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."
"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 product is very user-friendly."
"The best thing I like about Spring Cloud Data Flow is its plug-and-play model."
"The solution's most valuable feature is that it allows us to use different batch data sources, retrieve the data, and then do the data processing, after which we can convert and store it in the target."
"The ease of deployment on Kubernetes, the seamless integration for orchestration of various pipelines, and the visual dashboard that simplifies operations even for non-specialists such as quality analysts."
"The most valuable feature is real-time streaming."
"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 dashboards in Spring Cloud Dataflow are quite valuable."
 

Cons

"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."
"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."
"Lacks first in, first out queuing."
"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."
"The solution has a two-minute maximum time delay for live streaming, which could be reduced."
"The services which are described in the documentation could use some visual presentation because for someone who is new to the solution the documentation is not easy to follow or beginner friendly and can leave a person feeling helpless."
"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."
"There are certain shortcomings in the machine learning capacity offered by the product, 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."
"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."
"I would improve the dashboard features as they are not very user-friendly."
"Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required."
"The solution's community support could be improved."
"There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or refreshing the dashboard."
 

Pricing and Cost Advice

"Under $1,000 per month."
"Amazon Kinesis pricing is sometimes reasonable and sometimes could be better, depending on the planning, so it's a five out of ten for me."
"It was actually a fairly high volume we were spending. We were spending about 150 a month."
"In general, cloud services are very convenient to use, even if we have to pay a bit more, as we know what we are paying for and can focus on other tasks."
"I think for us, with Amazon Kinesis, if we have to set up our own Kafka or cluster, it will be very time-consuming. If one considers the aforementioned aspect, Amazon Kinesis is a cheap tool."
"The fee is based on the number of hours the service is running."
"The product falls on a bit of an expensive side."
"I rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive."
"This is an open-source product that can be used free of charge."
"The solution provides value for money, and we are currently using its community edition."
"If you want support from Spring Cloud Data Flow there is a fee. The Spring Framework is open-source and this is a free solution."
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Top Industries

By visitors reading reviews
Computer Software Company
18%
Financial Services Firm
17%
Manufacturing Company
10%
Educational Organization
4%
Financial Services Firm
26%
Computer Software Company
18%
Retailer
7%
Manufacturing Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Amazon Kinesis?
Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive.
What is your experience regarding pricing and costs for Amazon Kinesis?
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
What needs improvement with Amazon Kinesis?
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes. Also, the KCL library's documentation could be improved to better explain the configuration parameters...
What needs improvement with Spring Cloud Data Flow?
There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or r...
What is your primary use case for Spring Cloud Data Flow?
We had a project for content management, which involved multiple applications each handling content ingestion, transformation, enrichment, and storage for different customers independently. We want...
What advice do you have for others considering Spring Cloud Data Flow?
I would definitely recommend Spring Cloud Data Flow. It requires minimal additional effort or time to understand how it works, and even non-specialists can use it effectively with its friendly docu...
 

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis
No data available
 

Overview

 

Sample Customers

Zillow, Netflix, Sonos
Information Not Available
Find out what your peers are saying about Amazon Kinesis vs. Spring Cloud Data Flow and other solutions. Updated: June 2025.
860,592 professionals have used our research since 2012.