Spring Cloud Data Flow vs StreamSets comparison

VMware and IBM are both solutions in the Data Integration category. VMware is ranked #22 with an average rating of 7.6, while IBM is ranked #9 with an average rating of 8.4. VMware holds a 1.1% mindshare in DI, compared to IBM’s 1.7% 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

1,829 Views
1,384 Comparison Views

88% willing to recommend

StreamSets

Read 24 StreamSets reviews

3,109 Views
1,983 Comparison Views

100% willing to recommend

Spring Cloud Data Flow

StreamSets

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Spring Cloud Data Flow and StreamSets based on real PeerSpot user reviews.

Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

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

Buyer's Guide

Spring Cloud Data Flow vs. StreamSets

October 2024

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Categories and Ranking

Spring Cloud Data Flow

Ranking in Data Integration

22nd

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Streaming Analytics (10th)

StreamSets

Ranking in Data Integration

9th

Average Rating

8.4

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2024, in the Data Integration category, the mindshare of Spring Cloud Data Flow is 1.1%, up from 1.0% compared to the previous year. The mindshare of StreamSets is 1.7%, up from 1.3% 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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Reyansh Kumar

Technical Specialist at Accenture

We no longer need to hire highly skilled data engineers to create and monitor data pipelines

The things I like about StreamSets are its * overall user interface * efficiency * product features, which are all good. Also, 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. You just need to configure the data sources, the paths and their configurations, and you are ready to go. It is very efficient and very easy to use for ETL pipelines. It is a GUI-based interface in which you can easily create or design your own data pipelines with just a few clicks. As for moving data into modern analytics systems, we are using it with Microsoft Power BI, AWS, and some on-premises solutions, and it is very easy to get data from StreamSets into them. No hardcore coding or special technical expertise is required. It is also a no-code platform in which you can configure your data sources and data output for easy configuration of your data pipeline. This is a very important aspect because if a tool requires code development, we need to hire software developers to get the task done. By using StreamSets, it can be done with a few clicks.

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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 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 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 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."

"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 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 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."

"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."

"I really appreciate the numerous ready connectors available on both the source and target sides, the support for various media file formats, and the ease of configuring and managing pipelines centrally."

"It is a very powerful, modern data analytics solution, in which you can integrate a large volume of data from different sources. It integrates all of the data and you can design, create, and monitor pipelines according to your requirements. It is an all-in-one day data ops solution."

"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."

"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."

"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 UI is user-friendly, it doesn't require any technical know-how and we can navigate to social media or use it more easily."

More StreamSets pros

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."

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

"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."

"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."

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

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

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

"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."

"I would like to see further improvement in the UI. In addition, upgrades are not automatic and they should be automated. Currently, we have to manually upgrade versions."

"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 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."

"One area for improvement could be the cloud storage server speed, as we have faced some latency issues here and there."

"StreamSet works great for batch processing but we are looking for something that is more real-time. We need latency in numbers below milliseconds."

"We've seen a couple of cases where it appears to have a memory leak or a similar problem."

"Using ETL pipelines is a bit complicated and requires some technical aid."

More StreamSets cons

Pricing and Cost Advice

"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."

"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 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."

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

"The overall cost for small and mid-size organizations needs to be better."

"I believe the pricing is not equitable."

"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 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."

"It's not expensive because you pay per month, and the tasks you can perform with it are huge. It's reliable and cost-effective."

More StreamSets pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

29%

Computer Software Company

17%

Manufacturing Company

Retailer

Financial Services Firm

17%

Computer Software Company

13%

Manufacturing Company

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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...

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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...

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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

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?

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 ...

See all answers

What is your primary use case for StreamSets?

StreamSets is used for data transformation rather than ETL processes. It focuses on transforming data directly from sources without handling the extraction part of the process. The transformed data...

See all answers

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Compared 13% of the time

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Compared 12% of the time

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Compared 10% of the time

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Boomi iPaaS vs StreamSets

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Product Reports

Buyer's Guide

Spring Cloud Data Flow

October 2024

Download Spring Cloud Data Flow product report

Buyer's Guide

StreamSets

October 2024

Download StreamSets product report

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VMware

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IBM

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.

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.

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

October 2024

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: October 2024.

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