Amazon MSK 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 #6 with an average rating of 7.4, while VMware is ranked #10 with an average rating of 7.6. Amazon Web Services (AWS) holds a 9.2% mindshare in SA, compared to VMware’s 5.0% mindshare. Additionally, 90% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 88% of VMware users who would recommend it.

Amazon MSK

Read 10 Amazon MSK reviews

5,757 Views
4,452 Comparison Views

90% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

2,743 Views
2,051 Comparison Views

88% willing to recommend

Amazon MSK

Spring Cloud Data Flow

Comparison Buyer's Guide

Download the report

Executive Summary

We performed a comparison between Amazon MSK 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 MSK vs. Spring Cloud Data Flow Report (Updated: October 2024).

Buyer's Guide

Amazon MSK vs. Spring Cloud Data Flow

October 2024

Download the complete report

Helped 816,406 peers since 2012

Categories and Ranking

Amazon MSK

Ranking in Streaming Analytics

6th

Average Rating

7.4

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

Reviews Sentiment

6.7

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 MSK is 9.2%, down from 9.9% 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

FNU AKSHANSH

Senior Data Engineer at a computer software company with 201-500 employees

Streamlines our processes, and we don't need to configure any VPCs; it's automatic

We don't have many use cases involving ingesting large amounts of data and scaling up and down. We have a clear understanding of our data volume, which remains relatively constant throughout the week. While we're aware of other features Amazon MSK offers, we feel confident in our current setup. If our requirements change significantly in the future, we'll reassess our needs and consider adopting Amazon MSK.

Read full review

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.

Read full review

Quotes from Members

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

Pros

"It offers good stability."

"Amazon MSK has good integration because our team has been undergoing significant changes. Coupling it with MSK within AWS is helpful. We don't have to set up additionals or monitor external environments. This"

"Amazon MSK's separation of concerns and ease of creating and deploying new features are highly valuable. It just requires to assign them to the topic, and then anyone who needs to consume these messages can do so directly from Amazon MSK. This separation of concerns makes it very convenient, especially for new feature development, as developers can easily access the messages they need without having to deal with complex server communications or protocol setups."

"MSK has a private network that's an out-of-box feature."

"Amazon MSK has significantly improved our organization by building seamless integration between systems."

"Amazon MSK's scalability is very good."

"The solution's technical support was helpful."

"The most valuable feature of Amazon MSK is the integration."

More Amazon MSK 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 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."

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

"The product is very user-friendly."

"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."

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

Cons

"It does not autoscale. Because if you do keep it manually when you add a note to the cluster and then you register it, then it is scalable, but the fact that you have to go and do it, I think, makes it, again, a bit of some operational overhead when managing the cluster."

"The configuration seems a little complex and the documentation on the product is not available."

"Amazon MSK could improve on the features they offer. They are still lagging behind Confluence."

"The product's schema support needs enhancement. It will help enhance integration with many kinds of languages of programming languages, especially for environments using languages like .NET."

"In my opinion, there are areas in Amazon MSK that could be improved, particularly in terms of configuration. Initially setting it up and getting it connected was quite challenging. The naming conventions for policies were updated by AWS, and some were undocumented, leading to confusion with outdated materials. It took us weeks of trial and error before discovering new methods through hidden tutorials and official documentation."

"It should be more flexible, integration-wise."

"One of the reasons why we prefer Kafka is because the support is a little bit difficult to manage with Amazon MSK."

"It would be really helpful if Amazon MSK could provide a single installation that covers all the servers."

More Amazon MSK cons

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

"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."

"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."

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

"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 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."

Pricing and Cost Advice

"The platform has better pricing than one of its competitors."

"When you create a complete enterprise-driven architecture that is deployable on an enterprise scale, I would say that the prices of Amazon MSK and Confluent Platform become comparable."

"The price of Amazon MSK is less than some competitor solutions, such as Confluence."

"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."

"The solution provides value for money, and we are currently using its community edition."

"This is an open-source product that can be used free of charge."

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

816,406 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

21%

Computer Software Company

19%

Manufacturing Company

Retailer

Financial Services Firm

29%

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 MSK?

Amazon MSK has significantly improved our organization by building seamless integration between systems.

See all answers

What needs improvement with Amazon MSK?

From AWS, I would consider more MSK schema validation is needed. It is easy to integrate if you have an application, but on-topic integration is more complex. You can do it with EvenBridge very eas...

See all answers

What is your primary use case for Amazon MSK?

I have used Confluent Cloud and Amazon MSK in my company. We are not using it for analytics and it is more for CDC processes, so we change the capture processes. It is used to extract data from a d...

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

Comparisons

Azure Stream Analytics vs Amazon MSK

Compared 29% of the time

Confluent vs Amazon MSK

Compared 24% of the time

Amazon Kinesis vs Amazon MSK

Compared 16% of the time

Google Cloud Dataflow vs Amazon MSK

Compared 13% of the time

More Amazon MSK Competitors

Apache Flink vs Spring Cloud Data Flow

Compared 33% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 10% of the time

Google Cloud Dataflow vs Spring Cloud Data Flow

Compared 9% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 7% of the time

Azure Data Factory vs Spring Cloud Data Flow

Compared 5% of the time

More Spring Cloud Data Flow Competitors

Product Reports

Buyer's Guide

Amazon MSK

November 2024

Download Amazon MSK product report

Buyer's Guide

Spring Cloud Data Flow

October 2024

Download Spring Cloud Data Flow product report

Also Known As

Amazon Managed Streaming for Apache Kafka

No data available

Learn More

Amazon Web Services (AWS)

VMware

Overview

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Amazon MSK provides the control-plane operations, such as those for creating, updating, and deleting clusters.

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

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers

Information Not Available

Buyer's Guide

Amazon MSK vs. Spring Cloud Data Flow

October 2024

Free Report: Amazon MSK vs. Spring Cloud Data Flow

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

DOWNLOAD NOW

816,406 professionals have used our research since 2012.

See our Amazon MSK vs. Spring Cloud Data Flow report.

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.