Spring Cloud Data Flow vs Starburst Galaxy comparison

Why PeerSpot?

Broadcom and Starburst are both solutions in the Streaming Analytics category. Broadcom is ranked #17 with an average rating of 8.0, while Starburst is ranked #9 with an average rating of 9.3. Broadcom holds a 2.6% mindshare in SA, compared to Starburst’s 1.7% mindshare. Additionally, 88% of Broadcom users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

5,525 Views
1,658 Comparison Views

88% willing to recommend

Starburst Galaxy

Read 12 Starburst Galaxy reviews

2,464 Views
1,133 Comparison Views

100% willing to recommend

Spring Cloud Data Flow

Starburst Galaxy

Comparison Buyer's Guide

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Executive Summary

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

Find out in this report how the two Streaming Analytics 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. Starburst Galaxy Report (Updated: September 2026).

Buyer's Guide

Spring Cloud Data Flow vs. Starburst Galaxy

September 2026

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Helped 920,769 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 Streaming Analytics

17th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (33rd)

Starburst Galaxy

Ranking in Streaming Analytics

9th

Average Rating

9.4

Reviews Sentiment

2.6

Number of Reviews

Ranking in other categories

Data Science Platforms (7th)

Mindshare comparison

As of November 2026, in the Streaming Analytics category, the mindshare of Spring Cloud Data Flow is 2.6%, down from 4.6% compared to the previous year. The mindshare of Starburst Galaxy is 1.7%, up from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Starburst Galaxy	1.7%
Spring Cloud Data Flow	2.6%
Other	95.7%

Streaming Analytics

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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Pedromachado Ventura

Data Analyst at a financial services firm with 5,001-10,000 employees

Unified SQL layer has streamlined access to distributed historical data for analytics and reporting

One area that I think could be improved is the experience when performance issues occur. When a query is slow, it is not always immediately obvious to me whether the bottleneck comes from Starburst Galaxy itself, the underlying data source, the query design, or the reporting tool. Better visibility into query performance and easier diagnostics for non-administrators would be useful. Another potential improvement would be enhancing the experience with BI tools to make it more seamless. I work a lot with Power BI, and when you are working with larger data sets, performance can sometimes depend on several different layers. Having more visibility into what is happening between the BI tool, Starburst Galaxy, and the underlying source would be helpful. I also think onboarding could be a little more accessible for analysts. There is good technical documentation, but sometimes I just need to understand the best way to approach a common use case without diving too deep into the platform architecture. The main improvement would be troubleshooting. I have not used the AI capabilities extensively, so I cannot give a detailed assessment. I am not sure if my organization has the full capabilities of Starburst Galaxy, but I think adding AI on top of the data layer is interesting, especially if it can help users discover data, understand data sets, and interact with them more naturally. For governance and security, one of the strengths of Starburst Galaxy is that you can centralize access to data while still controlling what different users are allowed to see. Role-based access, fine-grained permissions, and data masking are important because giving people easier access to data should not mean giving everyone access to everything. I think that is even more important than any AI capabilities that are introduced. If you do introduce AI, I think it should respect exactly the same data permissions and governance rules as the user that is accessing the data.

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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 most valuable feature is real-time streaming."

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

"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."

More Spring Cloud Data Flow pros

"Starburst Galaxy has significantly improved our data architecture flexibility and performance management by solving cross-database query challenges and enabling us to utilize iceberg tables externally across our entire data ecosystem."

"The most fundamental feature is the query engine, which is much faster than any of the competitors; Starburst is able to finish most queries within 10 seconds, which is especially important for many non-technical employees."

"Starburst Galaxy has improved our organization by unifying access to all major data sources, reducing the need for complex ETL processes."

"Starburst Galaxy has positively impacted my organization by allowing us to rethink the strategy for data and architect data differently; instead of having multiple data marts and siloed data marts, we have a unified vision, and that is how it is changing."

"I use Starburst as a cost-efficient hosted option for Trino for data integration and ad-hoc analysis across a broad range of data sources."

"The main positive impact Starburst Galaxy has made is making data more accessible for analytics and reporting."

"Starburst Galaxy is becoming a cornerstone of our data platform, empowering us to make smarter and faster decisions across the organization."

"Starburst Galaxy serves as our primary SQL-based data processing engine, a strategic decision driven by its seamless integration with our AWS cloud infrastructure and its ability to deliver high performance with low-latency responses."

More Starburst Galaxy pros

Cons

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

"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."

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

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

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

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

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

More Spring Cloud Data Flow cons

"Multi-tenancy could be improved. In order to have multiple environments for SSO, we maintain multiple tenants that are connected to different AWS accounts via the Marketplace."

"As a hosted option, I wish I had more control over the cluster configuration, specifically regarding some of the more advanced options."

"Cluster startup time can be slow, sometimes taking over a minute."

"<p>Starburst Galaxy can be improved by discovering unstructured data and building in streaming ingestion because we are currently using Kafka for that purpose.</p>"

"I would like to see better alerting integrations for failures and errors in scheduled tasks and maintenance jobs."

"There is still room for improved usability and diagnostics, especially for users who are not platform specialists."

"I think there are areas of improvement with respect to AI adaptability, and also in general, the amount of connectors working with other tools are areas where it can be expanded."

"The most persistent issue is the cluster spin-up time."

More Starburst Galaxy 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."

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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

29%

Computer Software Company

10%

Manufacturing Company

Construction 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	4
Midsize Enterprise	2
Large Enterprise	4

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...

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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 Starburst Galaxy?

I recommend experimenting with different cluster sizes to determine what works best for your particular use case.

See all answers

What needs improvement with Starburst Galaxy?

See all answers

What is your primary use case for Starburst Galaxy?

I have mainly used Starburst Galaxy as a query and access layer for analytical data, as my current work involves dealing with data stored across different platforms, including Hive and SingleStore....

See all answers

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Product Reports

Buyer's Guide

Spring Cloud Data Flow

October 2026

Download Spring Cloud Data Flow product report

Buyer's Guide

Starburst Galaxy

October 2026

Download Starburst Galaxy 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

Starburst Galaxy offers rapid query speeds and robust cluster management, enhancing data engineering efficiency while supporting AWS integrations and cross-database functionality. Users benefit from its advanced data integration and federated querying capabilities.

Starburst Galaxy stands out with a compute-focused architecture that excels in facilitating seamless data integration. Technological innovations like autoscaling clusters and automated metadata management optimize operations in multi-tenant environments. With a keen emphasis on compatibility, the platform provides support for AWS Glue and enables federated querying across S3, Snowflake, and Redshift. This adaptability ensures comprehensive ETL processes and enhances analytics through querying SQL Server, Google Sheets, and blob stores. While noted for its robust capabilities, users seek improvements in cluster startup times, Tableau and AI support, and desire infrastructure-as-code enhancements.

What are Starburst Galaxy's key features?

Rapid Query Speeds: Enhances data processing with quick responses.
Cluster Autoscaling: Optimizes resource allocation for varying workloads.
Federated Querying: Cross-database functionality for seamless integration.
Compute-Focused Architecture: Ensures efficiency and scalability.
Automated Metadata Management: Streamlines data handling processes.
AWS Integration: Broad compatibility with AWS services.

What benefits should users evaluate in reviews?

Data Engineering Efficiency: Reduces operating expenses and improves productivity.
Cost Reduction: Highlights lower storage costs with efficient data handling.
Seamless Integration: Enables robust data integration across various platforms.
Cluster Management: Supports diverse workloads with advanced management tools.

In industries focusing on large-scale data efforts, Starburst Galaxy plays an essential role in connecting data sources like Amazon S3 and RDS, streamlining tasks in data engineering and ad-hoc analysis across complex environments. Teams leverage its cross-database querying to boost AWS analytics, with features tailored for sectors needing agile data solutions, from ETL pipelines to secure data federation.

Starburst

Buyer's Guide

Spring Cloud Data Flow vs. Starburst Galaxy

September 2026

Free Report: Spring Cloud Data Flow vs. Starburst Galaxy

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