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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
27
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 (24th)
 

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 8.7%, down from 13.9% compared to the previous year. The mindshare of Spring Cloud Data Flow is 5.0%, up from 4.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Rajni Kumar Jha - PeerSpot reviewer
Used for media streaming and live-streaming data
It is not compulsory to use Amazon Kinesis. If you don't want to use the data streaming, you can use just the Kinesis data firehose. Using the Kinesis data firehose is compulsory because we can't store all chats and recordings in Amazon S3 without it. When a call comes in the Amazon Kinesis instance, it will go to Data Streams if we use it. Otherwise, it will go to the Kinesis data firehose, where we need to define the S3 bucket path, and it will go to Amazon S3. So, without the Kinesis data firehose, we can't store all the chats and recordings in Amazon S3. Using Amazon Kinesis totally depends upon the user's requirements. If you want to use live streaming for the data lake or data analyst team, you need to use Amazon Kinesis. If you don't want to use it, you can directly use the Kinesis data firehose, which will be stored in Amazon S3. Overall, I rate the solution an eight out of ten.
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

"Great auto-scaling, auto-sharing, and auto-correction features."
"Amazon Kinesis has improved our ROI."
"The most valuable feature is that it has a pretty robust way of capturing things."
"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."
"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."
"The product's initial setup phase is not difficult because we are using the tool on the cloud."
"The feature that I've found most valuable is the replay. That is one of the most valuable in our business. We are business-to-business so replay was an important feature - being able to replay for 24 hours. That's an important feature."
"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."
"The product is very user-friendly."
"The best thing I like about Spring Cloud Data Flow is its plug-and-play model."
"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 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."
"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 feature is real-time streaming."
"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 dashboards in Spring Cloud Dataflow are quite valuable."
 

Cons

"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."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"If there were better documentation on optimal sharding strategies then it would be helpful."
"The solution has a two-minute maximum time delay for live streaming, which could be reduced."
"I think the default settings are far too low."
"We were charged high costs for the solution’s enhanced fan-out feature."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"Could include features that make it easier to scale."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
"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."
"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 is not an easy-to-use tool, so improvements are required."
"I would improve the dashboard features as they are not very user-friendly."
"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 solution's community support could be improved."
 

Pricing and Cost Advice

"The solution's pricing is fair."
"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."
"The tool's entry price is cheap. However, pricing increases with data volume."
"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 rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive."
"The tool's pricing is cheap."
"Under $1,000 per month."
"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 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."
"This is an open-source product that can be used free of charge."
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Top Industries

By visitors reading reviews
Computer Software Company
18%
Financial Services Firm
17%
Manufacturing Company
10%
Retailer
5%
Financial Services Firm
26%
Computer Software Company
17%
Manufacturing Company
7%
Retailer
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 is moderately priced. In comparison with other competitors, it is fairly priced, however, if they reduced the price a little, it could add more value to customers.
What needs improvement with Amazon Kinesis?
I do not see any scope for improvement as it does what it is supposed to do. No changes are required. Since it's predominantly a back-end service, any end-user isn't going to interact with it direc...
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: March 2025.
845,406 professionals have used our research since 2012.