Amazon Kinesis vs Spring Cloud Data Flow comparison

Amazon Web Services (AWS) and VMware are both solutions in the Streaming Analytics category. Amazon Web Services (AWS) is ranked #2 with an average rating of 7.9, while VMware is ranked #9 with an average rating of 7.7. Amazon Web Services (AWS) holds a 9.1% mindshare in SA, compared to VMware’s 4.8% mindshare. Additionally, 89% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 88% of VMware users who would recommend it.

Amazon Kinesis

Read 27 Amazon Kinesis reviews

6,323 Views
4,745 Comparison Views

89% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

2,241 Views
1,660 Comparison Views

88% willing to recommend

Amazon Kinesis

Spring Cloud Data Flow

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Spring Cloud Data Flow and Amazon Kinesis compete in the data stream processing category. Spring Cloud Data Flow has the upper hand in developer-friendly configurations and cost management, while Amazon Kinesis is preferred for its real-time scalability and strong AWS integration.

Features: Spring Cloud Data Flow supports integration with Spring projects, user-friendly configurations, and performs orchestration of microservices. Amazon Kinesis is known for real-time data processing, seamless scalability, and data integration with AWS services.

Room for Improvement: Spring Cloud Data Flow could enhance its scalability, improve ease of deployment, and offer a more robust integration with non-Spring systems. Amazon Kinesis might improve its cost-effectiveness, facilitate smoother multi-cloud integration, and provide enhanced flexibility in configurations.

Ease of Deployment and Customer Service: Amazon Kinesis offers cloud-based deployment with seamless AWS integration, while Spring Cloud Data Flow utilizes Kubernetes or Cloud Foundry, allowing more customization but complicating setup. Both have strong support, with Spring benefiting from its ecosystem, while Kinesis relies on AWS support.

Pricing and ROI: Spring Cloud Data Flow is generally more cost-effective due to its open-source nature, promoting resource optimization. Amazon Kinesis, though potentially more expensive, offers dependable performance and could deliver higher ROI with its data processing efficiency.

To learn more, read our detailed Amazon Kinesis vs. Spring Cloud Data Flow Report (Updated: March 2025).

Buyer's Guide

Amazon Kinesis vs. Spring Cloud Data Flow

March 2025

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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

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

Ranking in other categories

Data Integration (23rd)

Mindshare comparison

As of March 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 9.1%, down from 14.2% 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

Rajni Kumar Jha

Senior Software Engineer at JPMorgan Chase & Co.

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.

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NitinGoyal

Engineering Lead at Naukri.com

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.

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Quotes from Members

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

Pros

"I have worked in companies that build tools in-house. They face scaling challenges."

"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."

"Everything is hosted and simple."

"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 most valuable feature of Amazon Kinesis is real-time data streaming."

"The scalability is pretty good."

"I like the ease of use and how we can quickly get the configurations done, making it pretty straightforward and stable."

"Great auto-scaling, auto-sharing, and auto-correction features."

More Amazon Kinesis pros

"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 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."

"The best thing I like about Spring Cloud Data Flow is its plug-and-play model."

"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 product is very user-friendly."

"The dashboards in Spring Cloud Dataflow are quite valuable."

Cons

"The price is not much cheaper. So, there is room for improvement in the pricing."

"Could include features that make it easier to scale."

"Lacks first in, first out queuing."

"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."

"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."

"Amazon Kinesis should improve its limits."

"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."

"Kinesis can be expensive, especially when dealing with large volumes of data."

More Amazon Kinesis cons

"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."

"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 is not an easy-to-use tool, so improvements are required."

"I would improve the dashboard features as they are not very user-friendly."

"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."

"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."

"The solution's community support could be improved."

Pricing and Cost Advice

"The tool's entry price is cheap. However, pricing increases with data volume."

"The product falls on a bit of an expensive side."

"The tool's pricing is cheap."

"I rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive."

"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."

"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."

"The pricing depends on the use cases and the level of usage. If you wanted to use Kinesis for different use cases, there's definitely a cheaper base cost involved. However, it's not entirely cheap, as different use cases might require different levels of Kinesis usage."

"Under $1,000 per month."

More Amazon Kinesis pricing and cost advice

"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

19%

Financial Services Firm

17%

Manufacturing Company

Retailer

Financial Services Firm

25%

Computer Software Company

17%

Manufacturing Company

Retailer

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.

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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.

See all answers

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...

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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...

See all answers

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...

See all answers

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...

See all answers

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Product Reports

Buyer's Guide

Amazon Kinesis

March 2025

Download Amazon Kinesis product report

Buyer's Guide

Spring Cloud Data Flow

March 2025

Download Spring Cloud Data Flow product report

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

No data available

Overview

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.

Amazon Web Services (AWS)

Spring Cloud Data Flow is a toolkit for building data integration and real-time data processing pipelines.
Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics. Use Spring Cloud Data Flow to connect your Enterprise to the Internet of Anything—mobile devices, sensors, wearables, automobiles, and more.

VMware

Sample Customers

Zillow, Netflix, Sonos

Information Not Available

Buyer's Guide

Amazon Kinesis vs. Spring Cloud Data Flow

March 2025

Free Report: Amazon Kinesis vs. Spring Cloud Data Flow

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