Azure Data Factory vs Spring Cloud Data Flow comparison

Microsoft and Broadcom are both solutions in the Data Integration category. Microsoft is ranked #1 with an average rating of 8.2, while Broadcom is ranked #21 with an average rating of 7.8. Microsoft holds a 4.5% mindshare in DI, compared to Broadcom’s 1.2% 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 92 Azure Data Factory reviews

13,131 Views
9,192 Comparison Views

92% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

3,001 Views
1,230 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: November 2025).

Buyer's Guide

Azure Data Factory vs. Spring Cloud Data Flow

November 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

1st

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (2nd)

Spring Cloud Data Flow

Ranking in Data Integration

21st

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Streaming Analytics (10th)

Mindshare comparison

As of November 2025, in the Data Integration category, the mindshare of Azure Data Factory is 4.5%, down from 10.7% compared to the previous year. The mindshare of Spring Cloud Data Flow is 1.2%, 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	4.5%
Spring Cloud Data Flow	1.2%
Other	94.3%

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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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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Quotes from Members

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

Pros

"I find that the solution integrates well with cloud technologies, which we are using for different clouds like Snowflake and AWS"

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

"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."

"The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."

"Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."

"The function of the solution is great."

"I can do everything I want with SSIS and Azure Data Factory."

"Its integrability with the rest of the activities on Azure is most valuable."

More Azure Data Factory 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."

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

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

"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."

"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 dashboards in Spring Cloud Dataflow are quite valuable."

"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."

Cons

"Customer service is not satisfactory. Third-party personnel handle support and rely on a knowledge repository."

"We have experienced some issues with the integration. This is an area that needs improvement."

"Azure Data Factory uses many resources and has issues with parallel workflows."

"Data Factory could be improved in terms of data transformations by adding more metadata extractions."

"The inability to connect local VMs and local servers into the data flow is a limitation that prevents giving Azure Data Factory a perfect score."

"Azure Data Factory can improve by having support in the drivers for change data capture."

"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."

"I have encountered a problem with the integration with third-party solutions, particularly with SAP."

More Azure Data Factory cons

"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."

"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."

"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."

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

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

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

Pricing and Cost Advice

"Our licensing fees are approximately 15,000 ($150 USD) per month."

"The pricing model is based on usage and is not cheap."

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."

"The price is fair."

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."

"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."

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."

More Azure Data Factory 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

13%

Computer Software Company

12%

Manufacturing Company

Government

Financial Services Firm

24%

Computer Software Company

14%

Retailer

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	19
Large Enterprise	55

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...

See all answers

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...

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

Snowflake vs Azure Data Factory

Compared 8% of the time

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

Compared 6% of the time

Informatica PowerCenter vs Azure Data Factory

Compared 5% of the time

FME vs Azure Data Factory

Compared 5% of the time

Palantir Foundry vs Azure Data Factory

Compared 5% of the time

More Azure Data Factory Competitors

Apache Flink vs Spring Cloud Data Flow

Compared 36% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 8% of the time

MuleSoft Anypoint Platform vs Spring Cloud Data Flow

Compared 6% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 5% of the time

Oracle Data Integrator (ODI) vs Spring Cloud Data Flow

Compared 2% of the time

More Spring Cloud Data Flow Competitors

Product Reports

Buyer's Guide

Azure Data Factory

November 2025

Download Azure Data Factory product report

Buyer's Guide

Spring Cloud Data Flow

October 2025

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

November 2025

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