Azure Data Factory vs Spring Cloud Data Flow comparison

Microsoft and Broadcom are both solutions in the Data Integration category. Microsoft is ranked #3 with an average rating of 8.2, while Broadcom is ranked #20 with an average rating of 7.8. Microsoft holds a 3.2% mindshare in DI, compared to Broadcom’s 1.1% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

Azure Data Factory

Read 93 Azure Data Factory reviews

11,197 Views
7,838 Comparison Views

92% 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

Azure Data Factory

Spring Cloud Data Flow

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Executive SummaryUpdated on Dec 19, 2024

Spring Cloud Data Flow and Azure Data Factory are prominent products in the data processing arena. Azure Data Factory appears to have an edge with its diverse integrations and scalability options, whereas Spring Cloud Data Flow provides flexibility and support for Java-based complex workflows.

Features: Spring Cloud Data Flow supports microservice architectures and provides comprehensive data-processing capabilities, making it ideal for Java-based environments. It offers integration with Kubernetes and Docker, adding flexibility for various deployment scenarios. Azure Data Factory has a robust range of data connectors and integrates seamlessly with other Azure services. It provides a powerful user interface for creating ETL pipelines and supports real-time data processing workflows.

Room for Improvement: Spring Cloud Data Flow could enhance its ease of use for non-developers, improve integration with non-Java environments, and optimize its graphical interface for pipeline management. Azure Data Factory would benefit from improved documentation, expanded offline capabilities, and additional real-time processing features to enhance its usability outside the Azure ecosystem.

Ease of Deployment and Customer Service: Spring Cloud Data Flow offers deployment flexibility through Kubernetes and Docker, with strong community and enterprise support available. Azure Data Factory focuses on a cloud-first approach with global availability, ensuring a simpler deployment process and scalability. It benefits from Azure's comprehensive customer service infrastructure, often resulting in faster setup times.

Pricing and ROI: Spring Cloud Data Flow provides open-source access that can reduce costs, though enterprise support incurs additional fees. Azure Data Factory operates on a pay-as-you-go pricing model, aligning with scalability needs, offering potentially better ROI for larger deployments. While Spring Cloud Data Flow may lower initial costs, Azure Data Factory provides a predictable cost model for scaling environments.

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

Buyer's Guide

Azure Data Factory vs. Spring Cloud Data Flow

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

Azure Data Factory

Ranking in Data Integration

3rd

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (2nd)

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 Azure Data Factory is 3.2%, down from 10.0% 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 (%)
Azure Data Factory	3.2%
Spring Cloud Data Flow	1.1%
Other	95.7%

Data Integration

Featured Reviews

KandaswamyMuthukrishnan

Director at a computer software company with 1,001-5,000 employees

Integrates diverse data sources and streamlines ETL processes effectively

Regarding potential areas of improvement for Azure Data Factory, there is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration. Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters. I am satisfied with Azure Data Factory so far, but I suggest integrating some AI functionality to analyze data during the transition itself, providing insights such as null records, common records, and duplicates without running a separate pipeline or job. The monitoring tools in Azure Data Factory are helpful for optimizing data pipelines; while the current feature is adequate, they can improve by creating a live dashboard to see the online process, including how much percentage has been completed, which will be very helpful for people who are monitoring the pipeline.

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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

"The solution can scale very easily."

"The data is more scalable."

"Data Factory's most valuable feature is Copy Activity."

"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."

"It makes it easy to collect data from different sources."

"It is a complete ETL Solution."

"From what we have seen so far, the solution seems very stable."

"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."

More Azure Data Factory pros

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

"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 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 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."

"The product is very user-friendly."

"The most valuable feature is real-time streaming."

Cons

"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."

"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."

"Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost."

"The setup and configuration process could be simplified."

"There are performance issues, particularly with the underlying compute, which should be configurable."

"It would be better if it had machine learning capabilities."

"The pricing scheme is very complex and difficult to understand."

"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."

More Azure Data Factory cons

"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 is not an easy-to-use tool, so 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."

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

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

"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."

Pricing and Cost Advice

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."

"I don't see a cost; it appears to be included in general support."

"Pricing is comparable, it's somewhere in the middle."

"The pricing is a bit on the higher end."

"Azure Data Factory gives better value for the price than other solutions such as Informatica."

"The licensing cost is included in the Synapse."

"Data Factory is affordable."

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

More Azure Data Factory pricing and cost advice

"This is an open-source product that can be used free of charge."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

11%

Manufacturing Company

Government

Financial Services Firm

20%

Computer Software Company

13%

Retailer

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	19
Large Enterprise	57

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

Questions from the Community

How do you select the right cloud ETL tool?

AWS Glue and Azure Data factory for ELT best performance cloud services.

See all answers

How does Azure Data Factory compare with Informatica PowerCenter?

Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up an...

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How does Azure Data Factory compare with Informatica Cloud Data Integration?

Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...

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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

Snowflake vs Azure Data Factory

Compared 7% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 6% of the time

Palantir Foundry vs Azure Data Factory

Compared 5% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 4% of the time

FME vs Azure Data Factory

Compared 4% of the time

More Azure Data Factory 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

Amazon Kinesis vs Spring Cloud Data Flow

Compared 3% of the time

More Spring Cloud Data Flow Competitors

Product Reports

Buyer's Guide

Azure Data Factory

January 2026

Download Azure Data Factory product report

Buyer's Guide

Spring Cloud Data Flow

January 2026

Download Spring Cloud Data Flow product report

Overview

Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

Microsoft

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

Sample Customers

1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa

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Buyer's Guide

Azure Data Factory vs. Spring Cloud Data Flow

December 2025

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