Spring Cloud Data Flow vs Upsolver comparison

Broadcom and Upsolver are both solutions in the Data Integration category. Broadcom is ranked #31 with an average rating of 7.5, while Upsolver is ranked #38 with an average rating of 8.5. Broadcom holds a 1.0% mindshare in DI, compared to Upsolver’s 0.8% mindshare. Additionally, 88% of Broadcom users are willing to recommend the solution, compared to 100% of Upsolver users who would recommend it.

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

4,093 Views
2,251 Comparison Views

88% willing to recommend

Upsolver

Read 4 Upsolver reviews

1,819 Views
1,455 Comparison Views

100% willing to recommend

Spring Cloud Data Flow

Upsolver

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Spring Cloud Data Flow and Upsolver 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. Upsolver Report (Updated: June 2026).

Buyer's Guide

Spring Cloud Data Flow vs. Upsolver

June 2026

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Helped 907,731 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

31st

Ranking in Streaming Analytics

17th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Upsolver

Ranking in Data Integration

38th

Ranking in Streaming Analytics

21st

Average Rating

8.6

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2026, in the Data Integration category, the mindshare of Spring Cloud Data Flow is 1.0%, down from 1.2% compared to the previous year. The mindshare of Upsolver is 0.8%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Spring Cloud Data Flow	1.0%
Upsolver	0.8%
Other	98.2%

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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reviewer2784462

Software Engineer at a tech vendor with 10,001+ employees

Streaming pipelines have become simpler and onboarding new data sources is now much faster

One of the best features Upsolver offers is the automatic schema evolution. Another good feature is SQL-based streaming transformations. Complex streaming transformations such as cleansing, deduplication, and enrichment were implemented using SQL and drastically reduced the need for custom Spark code. My experience with the SQL-based streaming transformations in Upsolver is that it had a significant positive impact on the overall data engineering workflow. By replacing custom Spark streaming jobs with declarative SQL logic, I simplified development, review, and deployment processes. Data transformations such as parsing, filtering, enrichment, and deduplication could be implemented and modified quickly without rebuilding or redeploying complex code-based pipelines. Upsolver has impacted my organization positively because it brings many benefits. The first one is faster onboarding of new data sources. Another one is more reliable streaming pipelines. Another one is near-real-time data availability, which is very important for us. It also reduced operational effort for data engineering teams. A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days. Custom Spark code reduction reached 50 to 40 percent. Pipeline failures are reduced by 70 to 80 percent. Data latency is improved from hours to minutes.

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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

"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."

"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 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 product is very user-friendly."

"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 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."

More Spring Cloud Data Flow pros

"Customer service is excellent, and I would rate it between eight point five to nine out of ten."

"A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days, custom Spark code reduction reached 50 to 40 percent, pipeline failures are reduced by 70 to 80 percent, and data latency is improved from hours to minutes."

"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."

"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."

"I have saved 50 to 60% on maintaining pipelines since using Upsolver."

Cons

"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."

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

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

"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 documentation on offer is not that good."

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

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

More Spring Cloud Data Flow cons

"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."

"I would say Upsolver's scalability is eight out of 10 because of pricing."

"There is room for improvement in query tuning."

"On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it."

"Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future."

Pricing and Cost Advice

"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."

"Upsolver is affordable at approximately $225 per terabyte per year."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

10%

Retailer

Outsourcing Company

Real Estate/Law Firm

14%

Manufacturing Company

14%

Retailer

11%

Construction Company

11%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

No data available

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...

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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...

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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...

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What is your experience regarding pricing and costs for Upsolver?

My experience with pricing, setup cost, and licensing is that the pricing is nine out of 10.

See all answers

What needs improvement with Upsolver?

I think Upsolver can be improved with deeper integration with external orchestration out of the box. I would appreciate more clear dashboards with billing in real time as a needed improvement.

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What is your primary use case for Upsolver?

My main use case for Upsolver is to operate with changes in the structure of new data without a pipeline disrupting. I write SQL queries in Upsolver, and the platform takes care of the data itself,...

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Product Reports

Buyer's Guide

Spring Cloud Data Flow

July 2026

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Buyer's Guide

Data Integration

July 2026

Download Upsolver 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

Upsolver offers a data lakehouse platform that simplifies big data processing and analytics, enabling data teams to efficiently handle large-scale datasets.

Upsolver's platform focuses on simplifying the complexities of data engineering by transforming raw data into queryable formats quickly. It allows seamless integration with cloud storage and various database systems, providing flexibility for data-driven businesses. The platform automates data preparation tasks, reducing manual coding and allowing teams to focus on extracting insights. Its scalable architecture supports real-time analytics and batch processing.

What are the key features of Upsolver?

Data Ingestion: Easily ingest data from multiple sources with minimal configuration.
Schema On-Read: Automatically interpret data schemas, simplifying data management.
Streaming SQL: Transform and analyze streaming data using a familiar SQL syntax.
Data Lake Integration: Seamlessly connect with data lakes for streamlined workflows.

What benefits and ROI should users consider?

Time Savings: Faster data processing reduces time to insights.
Cost Efficiency: Scalable architecture optimizes infrastructure costs.
Scalability: Handle growing data volumes effortlessly.
Flexibility: Supports wide data storage and processing strategies.

In the e-commerce industry, Upsolver helps businesses optimize their data pipelines for better customer segmentation and personalization, while in finance, it enhances fraud detection capabilities through real-time data analytics.

Upsolver

Buyer's Guide

Spring Cloud Data Flow vs. Upsolver

June 2026

Free Report: Spring Cloud Data Flow vs. Upsolver

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