Spring Cloud Data Flow vs dbt comparison

dbt Labs and Broadcom are both solutions in the Data Integration category. dbt Labs is ranked #27 with an average rating of 7.5, while Broadcom is ranked #20 with an average rating of 7.8. dbt Labs holds a 1.7% mindshare in DI, compared to Broadcom’s 1.1% mindshare. Additionally, 100% of dbt Labs users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

dbt

Read 5 dbt reviews

1,642 Views
1,560 Comparison Views

100% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

3,055 Views
1,436 Comparison Views

88% willing to recommend

dbt

Spring Cloud Data Flow

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 19, 2024

Spring Cloud Data Flow and dbt are products in the data management category, focusing on processing workflows and transformation within pipelines, respectively. dbt appears to have the upper hand due to its robust features that justify the additional cost, despite some user preference for Spring Cloud Data Flow's pricing and support.

Features: Spring Cloud Data Flow provides real-time data processing capabilities, a microservices architecture, and a wide integration portfolio, making it suitable for complex data workflows. dbt specializes in SQL-based data transformation, offering automatic documentation, testing capabilities, and seamless integration with modern data warehouses. The primary distinction lies in data orchestration for Spring Cloud Data Flow compared to dbt's focus on transformation tasks.

Ease of Deployment and Customer Service: Spring Cloud Data Flow ensures straightforward deployment for microservices and integrates efficiently with cloud providers. In contrast, dbt offers a cloud-based deployment model, simplifying integration with current data platforms. Both products deliver strong customer service, but dbt's extensive community support enhances deployment, making it more accessible for data teams.

Pricing and ROI: Spring Cloud Data Flow offers competitive setup costs and favorable ROI for businesses focusing on extensive data orchestration, with cost efficiency through its open-source model. dbt, however, offers significant long-term ROI by optimizing data transformation processes despite its higher setup cost, with transformation efficiencies often outweighing the initial cost consideration.

To learn more, read our detailed Spring Cloud Data Flow vs. dbt Report (Updated: December 2025).

Buyer's Guide

Spring Cloud Data Flow vs. dbt

December 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

dbt

Ranking in Data Integration

27th

Average Rating

7.8

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Data Quality (17th)

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 (11th)

Mindshare comparison

As of January 2026, in the Data Integration category, the mindshare of dbt is 1.7%, up from 0.7% compared to the previous year. The mindshare of Spring Cloud Data Flow is 1.1%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Market Share Distribution
Product	Market Share (%)
Spring Cloud Data Flow	1.1%
dbt	1.7%
Other	97.2%

Data Integration

Featured Reviews

Shubham-Agarwal

Manager Projects at Cognizant

Incremental data models have cut full refresh time and support trusted executive reporting

I am not very familiar with dbt's version control system. I cannot identify any improvements in dbt because I am still exploring more functionality. I have been working with dbt for only three years, so I am exploring more functionalities and cannot see any limitations or improvement areas at this time. In the past, I used the seed functionality, which is used to load raw files, individual files, or static files into the database. Going forward, if dbt can improve or implement more features on the seed side, that would be beneficial, especially when we have large files available that take time to load the data into Snowflake database.

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Lipika Naik

Senior Software Engineer at QBE Regional Insurance

Provides ease of integration with other cloud platforms

Spring Cloud Data Flow is a useful product if I consider how there are different providers with whom my company had to deal, and most of them offer cloud-based products. I can't explain any crucial circumstances where the product's integration capabilities were helpful, but the aforementioned details explain the scenario for which I used the solution. I was only involved with the development of the product and not with the data pipeline configuration phase. The use of Spring Cloud Data Flow greatly impacted projects' time to market since our company's intention was to actually deploy and ensure that the payment platform integrated with it, which was an easy process. The product's user interface was very intuitive. The tool was deployed in multiple environments, but I am not sure about the production. From the time I started taking up the job in my current organization, I saw that we have deployed the tool in multiple environments wherein the number of users extensively used the product in the UAT environment, which is one of the most stable environments. There were 20 different methods to test the tool. I wouldn't be able to tell you the production details of the tool as I was more part of the production deployment, but I can say that it was deployed with the intent of making it available for 10,000 users. Those who plan to use the product should enjoy the flexibility of the solution. I rate the tool a nine 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

"There is operational efficiency achieved, and data quality and governance have also been achieved with modular SQL and version controlling, which reduced duplication of data and data errors."

"Since we migrated from SSIS to dbt model architecture, it takes around four hours only to complete a full refresh, and the client is now happy because our downtime was drastically reduced when we perform a complete refresh of the data."

"The product is developer-friendly."

"dbt has positively impacted my organization by allowing us to create our data pipelines much faster, going from ingestion of data to creating a data product in weeks instead of months, and we can do it in-house with the skillset we already have."

"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 most valuable feature is real-time streaming."

"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 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."

"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."

Cons

"Since dbt has a license cost, if a company is small and does not have much budget, they can explore other tools because there are other tools that provide the same functionality at a lower cost."

"dbt can be improved as I find the co-pilot in dbt is not very good, and my team has tried using it but opted to move off it and use other co-pilots such as GitHub."

"Dbt is not as stable as preferred, as it has had a few outages in the current year itself, so improvement should be made in the outages section as it is not stable."

"The solution must add more Python-based implementations."

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

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

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

"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."

"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."

"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."

Pricing and Cost Advice

"The solution’s pricing is affordable."

"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

14%

Insurance Company

Manufacturing Company

Computer Software Company

Financial Services Firm

20%

Computer Software Company

13%

Retailer

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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

Questions from the Community

What is your experience regarding pricing and costs for dbt?

My experience with pricing, setup cost, and licensing was simple enough.

See all answers

What needs improvement with dbt?

dbt can be improved as I find the co-pilot in dbt is not very good, and my team has tried using it but opted to move off it and use other co-pilots such as GitHub. Additionally, the debugging capab...

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What is your primary use case for dbt?

My main use case for dbt is for data transformation and data engineering.A specific example of how I use dbt for data transformation and engineering is that we use it to connect and ingest data fro...

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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

Comparisons

SSIS vs dbt

Compared 20% of the time

Oracle Data Integrator (ODI) vs dbt

Compared 9% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs dbt

Compared 7% of the time

Informatica PowerCenter vs dbt

Compared 7% of the time

Tobiko vs dbt

Compared 7% of the time

More dbt Competitors

Apache Flink vs Spring Cloud Data Flow

Compared 26% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 6% of the time

Apache Kafka vs Spring Cloud Data Flow

Compared 5% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 5% of the time

IBM InfoSphere DataStage vs Spring Cloud Data Flow

Compared 4% of the time

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Product Reports

Buyer's Guide

Data Integration

January 2026

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Buyer's Guide

Spring Cloud Data Flow

January 2026

Download Spring Cloud Data Flow product report

Overview

dbt is a transformational tool that empowers data teams to quickly build trusted data models, providing a shared language for analysts and engineering teams. Its flexibility and robust feature set make it a popular choice for modern data teams seeking efficiency.

Designed to integrate seamlessly with the data warehouse, dbt enables analytics engineers to transform raw data into reliable datasets for analysis. Its SQL-centric approach reduces the learning curve for users familiar with it, allowing powerful transformations and data modeling without needing a custom backend. While widely beneficial, dbt could improve in areas like version management and support for complex transformations out of the box.

What are the most valuable features of dbt?

SQL-Based Development: Facilitates transformations using SQL, streamlining the data modeling process.
Modularity: Encourages reusable components, making projects easier to manage and scale.
Version Control: Integrates with version control systems to track changes and collaborate effectively.
Testing Framework: Ensures data quality through automated tests, increasing trust in data outputs.

What benefits should you expect from using dbt?

Efficiency: Cuts down data processing times, allowing faster access to insights.
Collaboration: Promotes teamwork through shared data models and a common working language.
Scalability: Supports growing data demands by transforming complex data more flexibly and modularly.
Transparency: Provides clear documentation and lineage tracking, aiding in troubleshooting and understanding.

In the finance industry, dbt helps in cleansing and preparing transactional data for analysis, leading to more accurate financial reporting. In e-commerce, it empowers teams to rapidly integrate and analyze customer behavior data, optimizing marketing strategies and improving user experience.

dbt Labs

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

Buyer's Guide

Spring Cloud Data Flow vs. dbt

December 2025

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