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 7.8, while VMware is ranked #10 with an average rating of 7.6. Amazon Web Services (AWS) holds a 10.6% mindshare in SA, compared to VMware’s 5.0% mindshare. Additionally, 88% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 87% of VMware users who would recommend it.

Amazon Kinesis

Read 26 Amazon Kinesis reviews

8,771 Views
6,492 Comparison Views

88% willing to recommend

Spring Cloud Data Flow

Read 8 Spring Cloud Data Flow reviews

2,743 Views
2,051 Comparison Views

87% willing to recommend

Amazon Kinesis

Spring Cloud Data Flow

Comparison Buyer's Guide

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Executive Summary

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.

To learn more, read our detailed Amazon Kinesis vs. Spring Cloud Data Flow Report (Updated: October 2024).

Buyer's Guide

Amazon Kinesis vs. Spring Cloud Data Flow

October 2024

Download the complete report

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Categories and Ranking

Amazon Kinesis

Ranking in Streaming Analytics

2nd

Average Rating

8.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Spring Cloud Data Flow

Ranking in Streaming Analytics

10th

Average Rating

7.8

Number of Reviews

Ranking in other categories

Data Integration (22nd)

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 10.6%, down from 15.4% 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

Senior Software Engineer at JPMorgan Chase & Co.

Apr 3, 2024

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

Aug 15, 2024

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

"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."

"I find almost all features valuable, especially the timing and fast pace movement."

"One of the best features of Amazon Kinesis is the multi-partition."

"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."

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

"There is no problem with the tool's stability."

"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 integration capabilities of the product are good."

More Amazon Kinesis pros

"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 product is very user-friendly."

"The most valuable feature is real-time streaming."

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

"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."

"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."

Cons

"The solution has a two-minute maximum time delay for live streaming, which could be reduced."

"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."

"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."

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

"Amazon Kinesis should improve its limits."

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

"There are some kind of hard limits on Amazon Kinesis, and if you hit that, then you will get the throughput exceed error."

"If there were better documentation on optimal sharding strategies then it would be helpful."

More Amazon Kinesis cons

"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."

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

"Some of the features, like the monitoring tools, are not very mature and are still evolving."

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

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

"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."

"Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required."

Pricing and Cost Advice

"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."

"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 fee is based on the number of hours the service is running."

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

"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."

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

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

"Under $1,000 per month."

More Amazon Kinesis pricing and cost advice

"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

18%

Manufacturing Company

10%

Retailer

Financial Services Firm

29%

Computer Software Company

16%

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.

See all answers

What is your experience regarding pricing and costs for Amazon Kinesis?

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.

See all answers

What needs improvement with Amazon Kinesis?

There are some kind of hard limits on Amazon Kinesis, and if you hit that, then you will get the throughput exceed error. We have to deal with and reduce how many consumers are hitting Amazon Kines...

See all answers

What needs improvement with Spring Cloud Data Flow?

I would improve the dashboard features as they are not very user-friendly. Another area for improvement is the documentation, as it is not very precise. There are limited resources available on Spr...

See all answers

What is your primary use case for Spring Cloud Data Flow?

I am a developer using Spring Cloud Dataflow. We primarily use it to convert our applications from monolithic to microservices. The solution is used for scheduling tasks in a specific order and ens...

See all answers

What advice do you have for others considering Spring Cloud Data Flow?

My advice would be to thoroughly review the documentation and understand if Spring Cloud Dataflow is the right solution for your application. For applications with only one or two microservices, it...

See all answers

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Compared 7% of the time

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Product Reports

Buyer's Guide

Amazon Kinesis

November 2024

Download Amazon Kinesis product report

Buyer's Guide

Spring Cloud Data Flow

October 2024

Download Spring Cloud Data Flow product report

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

No data available

Learn More

Amazon Web Services (AWS)

VMware

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.

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.

Sample Customers

Zillow, Netflix, Sonos

Information Not Available

Buyer's Guide

Amazon Kinesis vs. Spring Cloud Data Flow

October 2024

Free Report: Amazon Kinesis vs. Spring Cloud Data Flow

Find out what your peers are saying about Amazon Kinesis vs. Spring Cloud Data Flow and other solutions. Updated: October 2024.

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