Spring Cloud Data Flow vs StreamSets comparison

VMware and IBM are both solutions in the Data Integration category. VMware is ranked #20 with an average rating of 7.8, while IBM is ranked #22 with an average rating of 9.0. VMware holds a 1.3% mindshare in DI, compared to IBM’s 1.6% mindshare. Additionally, 88% of VMware 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

3,216 Views
1,254 Comparison Views

88% willing to recommend

StreamSets

Read 21 StreamSets reviews

2,763 Views
1,989 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: July 2025).

Buyer's Guide

Spring Cloud Data Flow vs. StreamSets

July 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

Spring Cloud Data Flow

Ranking in Data Integration

20th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Streaming Analytics (9th)

StreamSets

Ranking in Data Integration

22nd

Average Rating

8.4

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2025, in the Data Integration category, the mindshare of Spring Cloud Data Flow is 1.3%, up from 0.9% compared to the previous year. The mindshare of StreamSets is 1.6%, up from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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.

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Ved Prakash Yadav

Senior Data Platform Manager at a manufacturing company with 10,001+ employees

Useful for data transformation and helps with column encryption

We use various tools and alerting systems to notify us of pipeline errors or failures. StreamSets supports data governance and compliance by allowing us to encrypt incoming data based on specified rules. We can easily encrypt columns by providing the column name and hash key. If you're considering using StreamSets for the first time, I would advise first understanding why you want to use it and how it will benefit you. If you're dealing with change tracking or handling large amounts of data, it could be cost-effective compared to services like Amazon. It's easy to schedule and manage tasks with the tool, and you can enhance your skills as an ETL developer. You can easily migrate traditional pipelines built on platforms like Informatica or Talend to StreamSets. I rate the overall solution an eight out of ten.

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

"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 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 most valuable feature is real-time streaming."

"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."

"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."

"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."

"One of the things I like is the data pipelines. They have a very good design. Implementing pipelines is very straightforward. It doesn't require any technical skill."

"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."

"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."

"StreamSets’ data drift resilience has reduced the time it takes us to fix data drift breakages. For example, in our previous Hadoop scenario, when we were creating the Sqoop-based processes to move data from source to destinations, we were getting the job done. That took approximately an hour to an hour and a half when we did it with Hadoop. However, with the StreamSets, since it works on a data collector-based mechanism, it completes the same process in 15 minutes of time. Therefore, it has saved us around 45 minutes per data pipeline or table that we migrate. Thus, it reduced the data transfer, including the drift part, by 45 minutes."

"The ETL capabilities are very useful for us. We extract and transform data from multiple data sources, into a single, consistent data store, and then we put it in our systems. We typically use it to connect our Apache Kafka with data lakes. That process is smooth and saves us a lot of time in our production systems."

"The best thing about StreamSets is its plugins, which are very useful and work well with almost every data source. It's also easy to use, especially if you're comfortable with SQL. You can customize it to do what you need. Many other tools have started to use features similar to those introduced by StreamSets, like automated workflows that are easy to set up."

More StreamSets pros

Cons

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

"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."

"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."

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

"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 software is very good overall. Areas for improvement are the error logging and the version history. I would like to see better, more detailed error logging information."

"I would like to see it integrate with other kinds of platforms, other than Java. We're going to have a lot of applications using .NET and other languages or frameworks. StreamSets is very helpful for the old Java platform but it's hard to integrate with the other platforms and frameworks."

"One thing that I would like to add is the ability to manually enter data. The way the solution currently works is we don't have the option to manually change the data at any point in time. Being able to do that will allow us to do everything that we want to do with our data. Sometimes, we need to manually manipulate the data to make it more accurate in case our prior bifurcation filters are not good. If we have the option to manually enter the data or make the exact iterations on the data set, that would be a good thing."

"We often faced problems, especially with SAP ERP. We struggled because many columns weren't integers or primary keys, which StreamSets couldn't handle. We had to restructure our data tables, which was painful. Also, pipeline failures were common, and data drifting wasn't addressed, which made things worse. Licensing was another issue we encountered."

"The documentation is inadequate and has room for improvement because the technical support does not regularly update their documentation or the knowledge base."

"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."

"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."

"If you use JDBC Lookup, for example, it generally takes a long time to process data."

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."

"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."

"StreamSets is an expensive 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."

"It has a CPU core-based licensing, which works for us and is quite good."

"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."

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

"There are different versions of the product. One is the corporate license version, and the other one is the open-source or free version. I have been using the corporate license version, but they have recently launched a new open-source version so that anybody can create an account and use it. The licensing cost varies from customer to customer. I don't have a lot of input on that. It is taken care of by PMO, and they seem fine with its pricing model. It is being used enterprise-wide. They seem to have got a good deal for StreamSets."

More StreamSets pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

16%

Retailer

Manufacturing Company

11%

Computer Software Company

10%

Financial Services Firm

10%

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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 do you like most about StreamSets?

The best thing about StreamSets is its plugins, which are very useful and work well with almost every data source. It's also easy to use, especially if you're comfortable with SQL. You can customiz...

See all answers

What needs improvement with StreamSets?

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 infr...

See all answers

What is your primary use case for StreamSets?

We are using StreamSets for batch loading.

See all answers

Comparisons

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Compared 6% of the time

Google Cloud Dataflow vs Spring Cloud Data Flow

Compared 5% of the time

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Informatica PowerCenter vs StreamSets

Compared 14% of the time

IBM InfoSphere DataStage vs StreamSets

Compared 12% of the time

webMethods.io vs StreamSets

Compared 9% of the time

Azure Data Factory vs StreamSets

Compared 7% of the time

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Product Reports

Buyer's Guide

Spring Cloud Data Flow

July 2025

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Buyer's Guide

StreamSets

July 2025

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.

VMware

StreamSets is a data integration platform that enables organizations to efficiently move and process data across various systems. It offers a user-friendly interface for designing, deploying, and managing data pipelines, allowing users to easily connect to various data sources and destinations. StreamSets also provides real-time monitoring and alerting capabilities, ensuring that data is flowing smoothly and any issues are quickly addressed.

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

July 2025

Free Report: Spring Cloud Data Flow vs. StreamSets

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