Spring Cloud Data Flow vs dbt comparison

dbt Labs and VMware are both solutions in the Data Integration category. dbt Labs is ranked #38 with an average rating of 7.5, while VMware is ranked #24 with an average rating of 7.7. Additionally, 100% of dbt Labs users are willing to recommend the solution, compared to 88% of VMware users who would recommend it.

dbt

Read 2 dbt reviews

611 Views
539 Comparison Views

100% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

1,421 Views
1,057 Comparison Views

88% willing to recommend

dbt

Spring Cloud Data Flow

Comparison Buyer's Guide

Download the report

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: April 2025).

Buyer's Guide

Spring Cloud Data Flow vs. dbt

April 2025

Download the complete report

Helped 847,772 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

dbt

Ranking in Data Integration

38th

Average Rating

7.6

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Spring Cloud Data Flow

Ranking in Data Integration

24th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Streaming Analytics (9th)

Featured Reviews

Ninad Magdum

Senior Machine Learning Engineer at Happiest Minds Technologies

Developer-friendly and easy to use, but doesn't have many optimization options

We also use stored procedures and Talend. They are not replaced by dbt completely. We use dbt only for the transformation process. My recommendations would depend on an organization’s requirements and problems. I will recommend the tool to others. The product is developer-friendly. However, it is still dependent on the data warehouse for big data and optimization. It's just a SQL transformation tool. It doesn't have a lot of optimization options like Spark. It's a good tool for Snowflake. If it were only for Snowflake, I would have rated it an eight out of ten. However, there are other data platforms. Overall, I rate the tool a six and a half out of ten.

Read full review

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.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The product is developer-friendly."

"The dashboards in Spring Cloud Dataflow are quite valuable."

"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 feature is real-time streaming."

"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."

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

Cons

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

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

"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."

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

"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."

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

Pricing and Cost Advice

"The solution’s pricing is affordable."

"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."

See which vendors are best for you

Use our free recommendation engine to learn which Data Integration solutions are best for your needs.

See recommendations

847,772 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

14%

Insurance Company

13%

Computer Software Company

Healthcare Company

Financial Services Firm

26%

Computer Software Company

17%

Retailer

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What is your experience regarding pricing and costs for dbt?

It is cheap because dbt is open source. If you compare the pay-per-service of Dbt with the open source option you can manage. We are managing the solution, when we were acquiring service from them....

See all answers

What needs improvement with dbt?

SQL statements that beyond DML, are not possible. Currently, they are not possible in Dbt. Having more features in SQL statements will support us. Another issue is the terms of data ingestion becau...

See all answers

What is your primary use case for dbt?

We use the solution to deal with data transformations inside different organizations.

See all answers

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 29% of the time

Informatica PowerCenter vs dbt

Compared 16% of the time

Tobiko vs dbt

Compared 8% of the time

Rivery vs dbt

Compared 7% of the time

Palantir Foundry vs dbt

Compared 7% of the time

More dbt Competitors

Apache Flink vs Spring Cloud Data Flow

Compared 42% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 11% of the time

Google Cloud Dataflow vs Spring Cloud Data Flow

Compared 7% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 5% of the time

MuleSoft Anypoint Platform vs Spring Cloud Data Flow

Compared 4% of the time

More Spring Cloud Data Flow Competitors

Product Reports

Buyer's Guide

Data Integration

March 2025

Download dbt product report

Buyer's Guide

Spring Cloud Data Flow

March 2025

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.

VMware

Buyer's Guide

Spring Cloud Data Flow vs. dbt

April 2025

Free Report: Spring Cloud Data Flow vs. dbt

Find out what your peers are saying about Spring Cloud Data Flow vs. dbt and other solutions. Updated: April 2025.

DOWNLOAD NOW

847,772 professionals have used our research since 2012.

See our Spring Cloud Data Flow vs. dbt report.

See our list of best Data Integration vendors.

We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.