Spring Cloud Data Flow vs StreamSets comparison

Why PeerSpot?

Broadcom and IBM are both solutions in the Data Integration category. Broadcom is ranked #33 with an average rating of 8.0, while IBM is ranked #30 with an average rating of 9.0. Broadcom holds a 1.1% 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

5,525 Views
3,205 Comparison Views

88% willing to recommend

StreamSets

Read 21 StreamSets reviews

5,056 Views
3,893 Comparison Views

100% willing to recommend

Spring Cloud Data Flow

StreamSets

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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: November 2026).

Buyer's Guide

Spring Cloud Data Flow vs. StreamSets

November 2026

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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

Spring Cloud Data Flow

Ranking in Data Integration

33rd

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Streaming Analytics (17th)

StreamSets

Ranking in Data Integration

30th

Average Rating

8.4

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2026, in the Data Integration category, the mindshare of Spring Cloud Data Flow is 1.1%, down from 1.1% compared to the previous year. The mindshare of StreamSets is 1.1%, down from 1.3% 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	1.1%
Other	97.8%

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."

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

"Overall, Spring Cloud Data Flow is a really good solution and a lot cheaper than a lot of infrastructure provided by big companies like Google or Amazon."

"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 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 dashboards in Spring Cloud Dataflow are quite valuable."

"The product is very user-friendly."

More Spring Cloud Data Flow pros

"The best feature that I really like is the integration."

"In StreamSets, everything is in one place."

"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."

"StreamSets data drift feature gives us an alert upfront so we know that the data can be ingested. Whatever the schema or data type changes, it lands automatically into the data lake without any intervention from us, but then that information is crucial to fix for downstream pipelines, which process the data into models, like Tableau and Power BI models. This is actually very useful for us. We are already seeing benefits. Our pipelines used to break when there were data drift changes, then we needed to spend about a week fixing it. Right now, we are saving one to two weeks. Though, it depends on the complexity of the pipeline, we are definitely seeing a lot of time being saved."

"It is really easy to set up and the interface is easy to use."

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

"It has helped us in moving into the data analytics world at a faster pace than any other tool would have done."

"The scheduling within the data engineering pipeline is very much appreciated, and it has a wide range of connectors for connecting to any data sources like SQL Server, AWS, Azure, etc. We have used it with Kafka, Hadoop, and Azure Data Factory Datasets. Connecting to these systems with StreamSets is very easy."

More StreamSets pros

Cons

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

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

"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."

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

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

"The documentation on offer is not that good."

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

More Spring Cloud Data Flow cons

"Visualization and monitoring need to be improved and refined."

"Currently, we can only use the query to read data from SAP HANA. What we would like to see, as soon as possible, is the ability to read from multiple tables from SAP HANA. That would be a really good thing that we could use immediately. For example, if you have 100 tables in SQL Server or Oracle, then you could just point it to the schema or the 100 tables and ingestion information. However, you can't do that in SAP HANA since StreamSets currently is lacking in this. They do not have a multi-table feature for SAP HANA. Therefore, a multi-table origin for SAP HANA would be helpful."

"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."

"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."

"There aren't enough hands-on labs, and debugging is also an issue because it takes a lot of time. Logs are not that clear when you are debugging, and you can only select a single source for a pipeline."

"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."

"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."

"Currently, we can only use the query to read data from SAP HANA. What we would like to see, as soon as possible, is the ability to read from multiple tables from SAP HANA."

More StreamSets cons

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."

"Its pricing is pretty much up to the mark. For smaller enterprises, it could be a big price to pay at the initial stage of operations, but the moment you have the Seed B or Seed C funding and you want to scale up your operations and aren't much worried about the funds, at that point in time, you would need a solution that could be scaled."

"There are two editions, Professional and Enterprise, and there is a free trial. We're using the Professional edition and it is competitively priced."

"The licensing is expensive, and there are other costs involved too. I know from using the software that you have to buy new features whenever there are new updates, which I don't really like. But initially, it was very good."

"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."

"StreamSets Data Collector is open source. One can utilize the StreamSets Data Collector, but the Control Hub is the main repository where all the jobs are present. Everything happens in Control Hub."

"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."

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

"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."

More StreamSets pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Outsourcing Company

Computer Software 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

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Product Reports

Buyer's Guide

Spring Cloud Data Flow

October 2026

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Buyer's Guide

StreamSets

October 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

November 2026

Free Report: Spring Cloud Data Flow vs. StreamSets

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