Spring Cloud Data Flow vs StreamSets comparison

Why PeerSpot?

Broadcom and IBM are both solutions in the Data Integration category. Broadcom is ranked #32 with an average rating of 8.0, while IBM is ranked #28 with an average rating of 9.0. Broadcom holds a 1.0% mindshare in DI, compared to IBM’s 1.1% mindshare. Additionally, 88% of Broadcom users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

5,078 Views
2,894 Comparison Views

88% willing to recommend

StreamSets

Read 21 StreamSets reviews

4,718 Views
3,633 Comparison Views

100% willing to recommend

Spring Cloud Data Flow

StreamSets

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 19, 2024

StreamSets and Spring Cloud Data Flow compete in the data integration and orchestration category. StreamSets offers advantages in user-friendliness and data transformation capabilities, making it the preferred choice for those emphasizing ease of use and flexibility in design.

Features: StreamSets offers a drag-and-drop interface for data pipeline creation, supports both batch and streaming data processing, and provides robust data transformation capabilities at the source. Its control hub facilitates graphical design with flexibility. Spring Cloud Data Flow is notable for its microservices orchestration, integration with Kubernetes, and support for low-resource execution using a simple programming model.

Room for Improvement: StreamSets needs better integration with non-Java platforms, enhanced memory management, and improvements in its security features and user interface. It also requires expanded SAP HANA capabilities and more effective data drift handling. Spring Cloud Data Flow could benefit from better community support, enhanced documentation, and improved UI for Kubernetes integration. Additional support for non-Java languages would also be beneficial.

Ease of Deployment and Customer Service: StreamSets provides versatile deployment options across public and hybrid clouds and generally responsive support, although complex issues may require time. Spring Cloud Data Flow is well-suited for private cloud environments but relies heavily on community forums for support, making professional assistance necessary for more challenging deployments. StreamSets facilitates easier cloud deployments, while Spring Cloud Data Flow is ideal for those adept at self-support in private solutions.

Pricing and ROI: StreamSets offers a range of licensing options, including open-source and commercial, but faces criticism for its cost structure, making it less attractive to smaller enterprises. It offers significant ROI for larger organizations in need of comprehensive ETL solutions. Spring Cloud Data Flow provides value through its free open-source model, being cost-effective for community edition users. StreamSets reports increased sales of up to 50%, while Spring Cloud Data Flow is ideal for budget-conscious users seeking open-source capabilities.

To learn more, read our detailed Spring Cloud Data Flow vs. StreamSets Report (Updated: September 2026).

Buyer's Guide

Spring Cloud Data Flow vs. StreamSets

September 2026

Download the complete report

Helped 917,403 peers since 2012

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

Spring Cloud Data Flow

Ranking in Data Integration

32nd

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Streaming Analytics (18th)

StreamSets

Ranking in Data Integration

28th

Average Rating

8.4

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2026, in the Data Integration category, the mindshare of Spring Cloud Data Flow is 1.0%, down from 1.1% compared to the previous year. The mindshare of StreamSets is 1.1%, down from 1.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
StreamSets	1.1%
Spring Cloud Data Flow	1.0%
Other	97.9%

Data Integration

Featured Reviews

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

SrinivasanSankar

Enterprise Solutions Architect at a energy/utilities company with 1,001-5,000 employees

Enables effective batch loading with visual interface and enterprise support

One issue I observed with StreamSets is that the memory runs out quickly when processing large volumes of data. Because of this memory issue, we have to upgrade our EC2 boxes in the Amazon AWS infrastructure. I had to switch to a new EC2 box, even though the processor was not fully utilized. It would be beneficial if StreamSets addressed any potential memory leak issues to prevent unnecessary upgrades. Additionally, it would be a great enhancement if StreamSets could produce a lineage graph to visualize how the data has passed through the system.

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

"This product will assist us in saving costs in many ways: No longer need to continue paying high fees for proprietary software, reduce the number of software engineers needed to support the product, and achieve faster time to market by using this product for our middleware."

"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 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 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."

"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 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."

More Spring Cloud Data Flow pros

"The entire user interface is very simple and the simplicity of creating pipelines is something that I like very much about it. The design experience is very smooth."

"The most valuable features are the option of integration with a variety of protocols, languages, and origins."

"I have used Data Collector, Transformer, and Control Hub products from StreamSets. What I really like about these products is that they're very user-friendly. People who are not from a technological or core development background find it easy to get started and build data pipelines and connect to the databases. They would be comfortable like any technical person within a couple of weeks."

"For me, the most valuable features in StreamSets have to be the Data Collector and Control Hub, but especially the Data Collector. That feature is very elegant and seamlessly works with numerous source systems."

"The most valuable would be the GUI platform that I saw. I first saw it at a special session that StreamSets provided towards the end of the summer. I saw the way you set it up and how you have different processes going on with your data. The design experience seemed to be pretty straightforward to me in terms of how you drag and drop these nodes and connect them with arrows."

"The ability to have a good bifurcation rate and fewer mistakes is valuable."

"The Ease of configuration for pipes is amazing. It has a lot of connectors. Mainly, we can do everything with the data in the pipe. I really like the graphical interface too"

"This product was a lot easier to use than the one we had before it, and it took us half an hour and we were set up and running it the first time."

More StreamSets pros

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."

"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 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."

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

"The visual user interface could use some help; it needs improvement."

"The documentation on offer is not that good."

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

More Spring Cloud Data Flow cons

"StreamSets should provide a mechanism to be able to perform data quality assessment when the data is being moved from one source to the target."

"The logging mechanism could be improved. If I am working on a pipeline, then create a job out of it and it is running, it will generate constant logs. So, the logging mechanism could be simplified. Now, it is a bit difficult to understand and filter the logs. It takes some time."

"They need to improve their customer care services. Sometimes it has taken more than 48 hours to resolve an issue. That should be reduced. They are aware of small or generic issues, but not the more technical or deep issues. For those, they require some time, generally 48 to 72 hours to respond. That should be improved."

"Sometimes, it is not clear at first how to set up nodes. A site with an explanation of how each node works would be very helpful."

"There are a few things that can be better."

"The execution engine could be improved. When I was at their session, they were using some obscure platform to run. There is a controller, which controls what happens on that, but you should be able to easily do this at any of the cloud services, such as Google Cloud. You shouldn't have any issues in terms of how to run it with their online development platform or design platform, basically their execution engine. There are issues with that."

"We create pipelines or jobs in StreamSets Control Hub. It is a great feature, but if there is a way to have a folder structure or organize the pipelines and jobs in Control Hub, it would be great. I submitted a ticket for this some time back."

"The monitoring visualization is not that user-friendly. It should include other features to visualize things, like how many records were streamed from a source to a destination on a particular date."

More StreamSets cons

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."

"I believe the pricing is not equitable."

"It's not so favorable for small companies."

"The pricing is affordable for any business."

"StreamSets is an expensive solution."

"We use the free version. It's great for a public, free release. Our stance is that the paid support model is too expensive to get into. They should honestly reevaluate that."

"We are running the community version right now, which can be used free of charge."

"The pricing is too fixed. It should be based on how much data you need to process. Some businesses are not so big that they process a lot of data."

"The overall cost is very flexible so it is not a burden for our organization... However, the cost should be improved. For small and mid-size organizations it might be a challenge."

More StreamSets pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Data Integration solutions are best for your needs.

See recommendations

917,403 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

Outsourcing Company

Comms Service Provider

Financial Services Firm

10%

Outsourcing Company

Comms Service Provider

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	1
Large Enterprise	5

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	11

Questions from the Community

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

What needs improvement with StreamSets?

See all answers

What is your primary use case for StreamSets?

We are using StreamSets for batch loading.

See all answers

What advice do you have for others considering StreamSets?

If asked, I definitely recommend StreamSets to other users. My overall rating for the solution is nine.

See all answers

Comparisons

Apache Flink vs Spring Cloud Data Flow

Compared 7% of the time

TIBCO Spotfire vs Spring Cloud Data Flow

Compared 6% of the time

Cloudera DataFlow vs Spring Cloud Data Flow

Compared 5% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 4% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 4% of the time

More Spring Cloud Data Flow Competitors

Pentaho Data Integration and Analytics vs StreamSets

Compared 9% of the time

Informatica PowerCenter vs StreamSets

Compared 6% of the time

SSIS vs StreamSets

Compared 6% of the time

Oracle Data Integrator (ODI) vs StreamSets

Compared 4% of the time

Versata Metatomix vs StreamSets

Compared 4% of the time

More StreamSets Competitors

Product Reports

Buyer's Guide

Spring Cloud Data Flow

September 2026

Download Spring Cloud Data Flow product report

Buyer's Guide

StreamSets

September 2026

Download StreamSets product report

Overview

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.

Broadcom

StreamSets streamlines data pipeline creation, connecting data from multiple sources to destinations like cloud platforms with minimal coding. Its centralized platform and intuitive design enhance ETL and data migration processes.

StreamSets integrates seamlessly with analytics platforms, offering tools such as Data Collector and Control Hub to facilitate data ingestion, transformation, and machine learning integrations. Its user-friendly interface and ready connectors aid in configuring complex data pipelines. With built-in data drift resilience and scheduling options, users experience efficient, scalable data management, despite challenges like latency in cloud storage and interface enhancement needs. Users often employ StreamSets for batch loading, real-time data processing, and smart data pipeline management, offering comprehensive data integration solutions.

What are the key features of StreamSets?

Data Collector: Enables streamlined data collection and processing.
Control Hub: Centralizes management of data pipelines.
Minimal Coding: Simplifies pipeline configuration with limited code requirement.
Data Drift Resilience: Adapts to changes in data patterns effectively.

What benefits should users look for?

Time-Saving: Efficient processes reduce manual effort.
Scalability: Easily manages large datasets.
Integration: Connects with leading analytics and cloud platforms.
Real-Time Processing: Supports continuous data delivery for timely insights.

In industries like finance and technology, StreamSets supports data migration, machine learning integrations, and analytics by simplifying data transformation and enhancing decision-making capabilities through its robust pipeline management.

IBM

Sample Customers

Information Not Available

Availity, BT Group, Humana, Deluxe, GSK, RingCentral, IBM, Shell, SamTrans, State of Ohio, TalentFulfilled, TechBridge

Buyer's Guide

Spring Cloud Data Flow vs. StreamSets

September 2026

Free Report: Spring Cloud Data Flow vs. StreamSets

Find out what your peers are saying about Spring Cloud Data Flow vs. StreamSets and other solutions. Updated: September 2026.

DOWNLOAD NOW

917,403 professionals have used our research since 2012.

See our Spring Cloud Data Flow vs. StreamSets report.

See our list of best Data Integration vendors.

We monitor all Data Integration 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.